init

tools/pretraining_data_builder/.gitignore

@@ -0,0 +1,9 @@
+.venv/
+.env
+**/*.pyc
+**/.pytest_cache
+search_data.json
+.idea
+*.zip
+*.jp2
+.ruff_cache

tools/pretraining_data_builder/README.md

@@ -0,0 +1,36 @@
+# Pretraining Data Builder
+This code is for building pretraining data for the self-supervised learning of SkySense++.
+
+## Install
+Prepare the environment:
+```
+conda create -n data_builder python=3.12
+conda activate data_builder
+pip install -r requirements.txt
+```
+Download pretraining data list in lmdb format from [Zenodo](https://zenodo.org/records/14994430)
+
+## Download Data
+```
+python -m rsi_download --username <username> --password <password> --api_key <api_key> <X> <Y> <Z> <date_min> <date_max>
+```
+Notes:
+1. `username` and `password` can be created in the [Copernicus Data Space Ecosystem](https://data.copernicus.eu/cdsapp/#!/home), 
+`api_key` can be created in the [Maxar](https://ard.maxar.com/docs/about/).
+2. `X` `Y` `Z` are coordinates in the Web Mercator coordinate system.
+3. `date_min` and `date_max` are in the format of `YYYY-MM`.
+
+## Process Data
+```
+python -m rsi_process --platform <platform> --fn_img path/to/image.zip --save_dir output_<platform>/
+```
+Notes:
+1. `platform` can be `s1`, `s2`, `wv`.
+2. `fn_img` is the path to the downloaded zip file.
+3. `save_dir` is the directory to save the processed data.
+
+## Automatic Script
+```
+sh run_data_builder.sh
+```
+This script will first read the pretraining list, then download the data according to the list, and proceed them automatically.

tools/pretraining_data_builder/requirements.txt

@@ -0,0 +1,17 @@
+httpx>=0.27.2
+python-dotenv>=1.0.0
+orjson>=3.9.10
+rich>=13.7.0
+click>=8.1.7
+msgspec>=0.18.4
+asyncclick>=8.1.3.4
+numpy
+gdal
+pyproj
+mercantile
+Pillow
+shapely
+imageio
+geopandas
+pyresample
+lmdb

tools/pretraining_data_builder/rsi_download/__main__.py

@@ -0,0 +1,76 @@
+import click
+from rsi_download.download_async import download_core
+import asyncio
+
+@click.command()
+@click.argument("x", type=click.STRING)
+@click.argument("y", type=click.STRING)
+@click.argument("z", type=click.STRING)
+@click.argument("date_min", type=click.STRING)
+@click.argument("date_max", type=click.STRING)
+@click.option(
+    "--username",
+    "-u",
+    type=click.STRING,
+    help="Username for Copernicus Data Space Ecosystem",
+)
+@click.option(
+    "--password", "-p", prompt=True, hide_input=True, confirmation_prompt=False
+)
+@click.option(
+    "--api_key", "-k", prompt=True, hide_input=True, confirmation_prompt=False
+)
+@click.option(
+    "--max",
+    "-m",
+    "max_",
+    default=100,
+    type=click.INT,
+    show_default=True,
+    help="maximum number of results returned",
+)
+@click.option(
+    "--cloud-coverage",
+    "-c",
+    "cloud_coverage",
+    default=10.00,
+    type=click.FLOAT,
+    show_default=True,
+    help="Get only results with a cloud coverage percentage less then the argument given.",
+)
+
+@click.option(
+    "--platform-name",
+    "-n",
+    "platform_name",
+    default="S2",
+    type=click.Choice(["S2", "S1", "WV3"]),
+    show_default=True,
+    help="Get only results with a platform name.",
+)
+
+@click.option(
+    "--debug",
+    default=False,
+    is_flag=True,
+    type=click.BOOL,
+    show_default=True,
+    help="Debug the http requests and extra debug logging",
+)
+@click.option(
+    "--tci",
+    default=False,
+    is_flag=True,
+    type=click.BOOL,
+    show_default=True,
+    help="Download only True Color Image (TCI)",
+)
+
+def main(x, y, z, date_min, date_max, username, password, api_key, max_, cloud_coverage, debug, tci, platform_name):
+    return asyncio.run(download_core(x, y, z, date_min, date_max, username, password, api_key, max_, cloud_coverage, debug, tci, platform_name))
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("\n程序已终止")

tools/pretraining_data_builder/rsi_download/auth.py

@@ -0,0 +1,36 @@
+import httpx
+import msgspec
+
+
+class CDSETokens(msgspec.Struct):
+    """Copernicus Data Space Ecosystem Tokens"""
+
+    access_token: str
+    refresh_token: str
+    expires_in: int
+    refresh_expires_in: int
+    token_type: str
+    not_before_policy: int = msgspec.field(name="not-before-policy")
+    session_state: str
+    scope: str
+
+
+def get_access_token(username: str, password: str) -> CDSETokens:
+    data = {
+        "client_id": "cdse-public",
+        "username": username,
+        "password": password,
+        "grant_type": "password",
+    }
+    try:
+        with httpx.Client() as client:
+            r = client.post(
+                "https://identity.dataspace.copernicus.eu/auth/realms/CDSE/protocol/openid-connect/token",
+                data=data,
+            )
+            r.raise_for_status()
+    except Exception as e:
+        raise Exception(
+            f"Access token creation failed: {e}. Reponse from the server was: {r.json()}"
+        )
+    return msgspec.json.decode(r.content, type=CDSETokens)

tools/pretraining_data_builder/rsi_download/cli.py

@@ -0,0 +1,133 @@
+from typing import Tuple, List
+
+from rich.table import Table
+from rich.console import Console
+import re
+import msgspec
+from datetime import datetime
+from rich.progress import (
+    BarColumn,
+    DownloadColumn,
+    Progress,
+    TextColumn,
+    TimeRemainingColumn,
+    TransferSpeedColumn,
+)
+from rsi_download.exceptions import InvalidWktPointArgument, InvalidDateRangeArgument
+from rsi_download.download.search import SearchContent, SearchResult
+
+
+class Preview(msgspec.Struct):
+    id: str
+    productid: str
+    url: str
+    origin_date: str
+    name: str
+
+
+progress = Progress(
+    TextColumn("[bold blue]{task.fields[filename]}", justify="right"),
+    BarColumn(bar_width=None),
+    "[progress.percentage]{task.percentage:>3.1f}%",
+    "•",
+    DownloadColumn(),
+    "•",
+    TransferSpeedColumn(),
+    "•",
+    TimeRemainingColumn(),
+)
+
+
+# "2022-05-03T00:00:00.000Z"
+ESA_DATE_FORMAT = "%Y-%m-%dT%H:%M:%S.%fZ"
+
+
+def convert_to_timestamp(datestring="", dateformat="%d-%m-%Y %H:%M:%S") -> str:
+    if len(datestring) > 10:
+        source = datetime.strptime(datestring, dateformat)
+    else:
+        source = datetime.strptime(datestring, "%d-%m-%Y")
+    return source.strftime(ESA_DATE_FORMAT)
+
+
+def daterange_to_timestamp(daterange: str) -> Tuple[str, str]:
+    if "," not in daterange:
+        raise InvalidDateRangeArgument(
+            f'Give a valid daterange string. for example: "11-08-2023 00:00:00,11-09-2023 00:00:00" \n Daterange received: {daterange}'
+        )
+    gt, lt = daterange.split(",")
+    try:
+        time_gt = convert_to_timestamp(datestring=gt)
+    except ValueError:
+        raise InvalidDateRangeArgument(
+            f"Invalid dateformat encountered for time_gt: {gt}. Dateformat expected: %d-%m-%Y or %d-%m-%Y %H:%M:%S"
+        )
+    try:
+        time_lt = convert_to_timestamp(datestring=lt)
+    except ValueError:
+        raise InvalidDateRangeArgument(
+            f"Invalid dateformat encountered for time_lt: {lt}. Dateformat expected: %d-%m-%Y or %d-%m-%Y %H:%M:%S"
+        )
+    return time_gt, time_lt
+
+
+def wkt_to_point(wktstring: str) -> Tuple[float, ...]:
+    nums = re.findall(r"[-+]?\d*\.\d+|\d+", wktstring)
+    if len(nums) != 2:
+        raise InvalidWktPointArgument(
+            f"Give a valid WKT string. for example: POINT(-9.1372 38.7000). WKT received: {wktstring}"
+        )
+    return tuple(float(n) for n in nums)
+
+
+def show_preview_urls(search_json: SearchContent, platform_name: str) -> List[Preview]:
+    """
+    Show a list of preview urls for downloading in the terminal
+
+    :param search_json: SearchContent object
+    """
+    # print(search_json.value)
+    preview_urls = [
+        Preview(
+            id=str(i),
+            productid=v.id,
+            url=v.assets[0].download_link,
+            origin_date=v.content_date.start,
+            name=v.name,
+        )
+        for i, v in enumerate(search_json.value)
+    ]
+    table = Table(title=f"RSI Preview Url's")
+    table.add_column("ID", justify="left", style="magenta")
+    table.add_column("Acquisition Time", justify="left", style="blue")
+    table.add_column("Name", justify="left", style="magenta")
+
+    for entry in preview_urls:
+        table.add_row(
+            entry.id,
+            f'[link={entry.url.replace("(", "%28").replace(")", "%29")}]{entry.origin_date}[/link]',
+            entry.name,
+        )
+
+    console = Console()
+    console.print(table)
+    return preview_urls
+
+
+def get_selected_products(
+    search_json: SearchContent, preview_urls: List[Preview], product_ids: str
+) -> List[SearchResult]:
+    """
+    Return the selected items from the search_json by the preview url id.
+
+    :param search_json: SearchContent
+    :param preview_urls: List[Preview]
+    :param product_ids: string of preview ids
+    :return: List[SearchResult]
+    """
+    download_product_ids = [
+        item.productid
+        for item in preview_urls
+        if item.id in [str(n) for n in product_ids]
+    ]
+    return [x for x in search_json.value if x.id in download_product_ids]

tools/pretraining_data_builder/rsi_download/download/product.py

@@ -0,0 +1,97 @@
+import asyncio
+from typing import List
+import signal
+import httpx
+from rich.progress import TaskID, Event
+from rsi_download.cli import progress
+from rsi_download.download.search import SearchResult
+from rsi_download.cli import Preview
+import os
+
+done_event = Event()
+
+
+def handle_sigint(signum, frame):
+    done_event.set()
+
+
+signal.signal(signal.SIGINT, handle_sigint)
+
+
+async def download_tci_products_data(
+    task_id: TaskID, product: SearchResult, access_token: str, mm_band: str = "R10m"
+):
+    headers = {"Authorization": f"Bearer {access_token}"}
+    progress.start_task(task_id)
+    async with httpx.AsyncClient() as client:
+        client.headers.update(headers)
+        # create the tci image url
+        granule_url = f"https://zipper.dataspace.copernicus.eu/odata/v1/Products({product.id})/Nodes({product.name})/Nodes(GRANULE)/Nodes"
+        granule_resp = await client.get(
+            f"{granule_url}", follow_redirects=True, headers=headers
+        )
+        granule_folder = granule_resp.json()
+        img_data_url = f"{granule_url}({granule_folder['result'][0]['Name']})/Nodes(IMG_DATA)/Nodes({mm_band})/Nodes"
+        img_data_resp = await client.get(img_data_url, follow_redirects=True)
+        img_data = img_data_resp.json()
+        tci_name = [img["Name"] for img in img_data["result"] if "TCI" in img["Name"]][
+            0
+        ]
+        tci_url = f"{img_data_url}({tci_name})/$value"
+        async with client.stream(
+            method="GET",
+            url=tci_url,
+            headers=headers,
+        ) as response:
+            progress.update(task_id, total=int(response.headers["Content-length"]))
+            with open(f"{tci_name}", "wb") as file:
+                progress.start_task(task_id)
+                async for chunk in response.aiter_bytes():
+                    if chunk:
+                        file.write(chunk)
+                        progress.update(task_id, advance=len(chunk))
+                        if done_event.is_set():
+                            return
+        progress.console.log(f"Downloaded {tci_name}")
+
+
+async def download_data(task_id: TaskID, product: SearchResult, preview: Preview, access_token: str):
+    headers = {"Authorization": f"Bearer {access_token}"}
+    async with httpx.AsyncClient() as client:
+        client.headers.update(headers)
+        async with client.stream(
+            "GET",
+            url=f"https://zipper.dataspace.copernicus.eu/odata/v1/Products({product.id})/$value",
+            headers=headers,
+        ) as response:
+            progress.update(task_id, total=int(response.headers["Content-length"]))
+            with open(f"out_raw/{preview.name.replace('.SAFE', '.zip')}", "wb") as file:
+                progress.start_task(task_id)
+                async for chunk in response.aiter_bytes():
+                    if chunk:
+                        file.write(chunk)
+                        progress.update(task_id, advance=len(chunk))
+                        if done_event.is_set():
+                            return
+    progress.console.log(f"Downloaded {preview.name.replace('.SAFE', '.zip')}")
+
+async def download_products_data(
+    products: List[SearchResult], previews: List[Preview], access_token: str, tci_only: bool = False
+):
+    with progress:
+        download_tasks = []
+        for product, preview in zip(products, previews):
+            task_id = progress.add_task(
+                f"{preview.name.replace('.SAFE', '.zip')}",
+                filename=f"{preview.name.replace('.SAFE', '.zip')}",
+                start=False,
+            )
+            if tci_only:
+                download_tasks.append(
+                    download_tci_products_data(task_id, product, access_token)
+                )
+            else:
+                download_tasks.append(download_data(task_id, product, preview, access_token))
+            # os.rename(f"product-{product.id}.zip", f"{preview.name.replace('.SAFE', '.zip')}")
+        await asyncio.gather(*download_tasks)
+

tools/pretraining_data_builder/rsi_download/download/search.py

@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+from typing import List
+
+import msgspec
+import httpx
+
+from rsi_download.exceptions import SearchException
+from rsi_download.geo.geo_types import GeoJsonPolygon
+
+ESA_SEARCH_URL = r"https://catalogue.dataspace.copernicus.eu/odata/v1/Products"
+
+
+class ContentData(msgspec.Struct, rename="pascal"):
+    """Odata search result start and end date"""
+
+    start: str
+    end: str
+
+
+class Asset(msgspec.Struct, rename="pascal"):
+    """Odata search Asset"""
+
+    type_: str = msgspec.field(name="Type")
+    id: str
+    download_link: str
+    s3_path: str
+
+
+class SearchResult(msgspec.Struct, rename="pascal"):
+    """Odata search Result"""
+
+    id: str
+    name: str
+    content_length: int
+    origin_date: str
+    s3_path: str
+    content_date: ContentData
+    geo_footprint: GeoJsonPolygon
+    assets: List[Asset]
+
+
+class SearchContent(msgspec.Struct):
+    value: List[SearchResult]
+    next_link: str | None = msgspec.field(default=None, name="@odata.nextLink")
+
+
+async def search_odata(
+    long: float,
+    lat: float,
+    cloud_coverage: float,
+    time_lt: str,
+    time_gt: str,
+    max_: int,
+    platform_name: str,
+) -> SearchContent:
+    # filter voor zoeken op cloudCover, Productype en orbitDirection.
+    # lt = less then
+    # eq = equal to
+    # gt = greater then
+    # sentinel-2
+    if platform_name == "S2":
+        search_filter = f"OData.CSC.Intersects(area=geography'SRID=4326;POINT ({long:.4f} {lat:.4f})') and Attributes/OData.CSC.DoubleAttribute/any(att:att/Name eq 'cloudCover' and att/OData.CSC.DoubleAttribute/Value lt {cloud_coverage:.2f}) and Attributes/OData.CSC.StringAttribute/any(att:att/Name eq 'productType' and att/OData.CSC.StringAttribute/Value eq 'S2MSI2A') and ContentDate/Start gt {time_gt} and ContentDate/Start lt {time_lt}"
+    elif platform_name == "S1":
+        search_filter = f"OData.CSC.Intersects(area=geography'SRID=4326;POINT ({long:.4f} {lat:.4f})') and Attributes/OData.CSC.StringAttribute/any(att:att/Name eq 'productType' and att/OData.CSC.StringAttribute/Value eq 'IW_GRDH_1S') and ContentDate/Start gt {time_gt} and ContentDate/Start lt {time_lt}"
+    elif platform_name == "WV3":
+        search_filter = f"OData.CSC.Intersects(area=geography'SRID=4326;POINT ({long:.4f} {lat:.4f})') and Attributes/OData.CSC.StringAttribute/any(att:att/Name eq 'platformName' and att/OData.CSC.StringAttribute/Value eq 'WorldView-3') and ContentDate/Start gt {time_gt} and ContentDate/Start lt {time_lt}"
+    else:
+        raise ValueError(f"Invalid platform name: {platform_name}")
+
+    async with httpx.AsyncClient() as client:
+        r = await client.get(
+            url=f"{ESA_SEARCH_URL}?$filter={search_filter}&$top={max_}&$expand=Assets",
+            timeout=60,
+        )
+        if not r.status_code == 200:
+            raise SearchException(f"Error getting data: {r.text}")
+        return msgspec.json.decode(r.content, type=SearchContent)

tools/pretraining_data_builder/rsi_download/download_async.py

@@ -0,0 +1,100 @@
+from typing import List, Tuple
+
+import msgspec
+import asyncio
+from rich import print
+from rsi_download.auth import get_access_token
+from rsi_download.download.product import download_products_data
+from rsi_download.cli import (
+    show_preview_urls,
+    Preview,
+    get_selected_products,
+)
+from rsi_download.download.search import search_odata
+import math
+
+
+
+async def download_core(
+    x: str,
+    y: str,
+    z: str,
+    date_min: str,
+    date_max: str,
+    username: str,
+    password: str,
+    api_key: str = None,
+    max_: int = 100,
+    cloud_coverage: float = 20.0,
+    debug: bool = False,
+    tci: bool = False,
+    platform_name: str = "S2",
+):
+    """
+    X tile x coordinate
+    Y tile y coordinate 
+    Z zoom level
+    DATE_MIN start date in format YYYYMM
+    DATE_MAX end date in format YYYYMM
+    """
+    lat, long = tile_to_latlon(float(x), float(y), float(z))
+    time_gt = f"{date_min[:4]}-{date_min[4:6]}-01T00:00:00.000Z"
+    year = int(date_max[:4])
+    month = int(date_max[4:])
+    if month == 12:
+        next_year = year + 1
+        next_month = 1
+    else:
+        next_year = year
+        next_month = month + 1
+    time_lt = f"{next_year}-{next_month:02d}-01T00:00:00.000Z"
+    
+    print(f"coordinates: lat: {lat:.4f}, long: {long:.4f}")
+    print(f"maximum results: {max_}")
+    print(f"cloud coverage percentage less then: {cloud_coverage:.2f}")
+    print(f"time_gt: {time_gt}, time_lt: {time_lt}")
+    search_data = await search_odata(long, lat, cloud_coverage, time_lt, time_gt, max_, platform_name)
+    if debug:
+        print("DEBUG: Search request data is saved to disk.")
+        with open("search_data.json", "wb") as f:
+            f.write(msgspec.json.encode(search_data))
+    preview_urls: List[Preview] = show_preview_urls(search_data, platform_name)
+    print("start downloading all data ...")
+    products_to_download = get_selected_products(
+        search_json=search_data, preview_urls=preview_urls, product_ids=list(range(len(preview_urls)))
+    )
+    tokens = get_access_token(username, password)
+
+    try:
+        for i, (product, preview) in enumerate(zip(products_to_download, preview_urls)):
+            print(f"[{i+1}/{len(products_to_download)}] downloading {product.id} ...")
+            await asyncio.shield(download_products_data(
+                [product], [preview], tokens.access_token, tci_only=tci
+            ))
+    except asyncio.CancelledError:
+        print("\nDownload cancelled, exiting...")
+        return
+
+def tile_to_latlon(x: int, y: int, z: int, get_center: bool = True) -> Tuple[float, float]:
+    """
+    Convert XYZ tile coordinates to latitude/longitude
+    
+    Args:
+        x: Tile X coordinate
+        y: Tile Y coordinate
+        z: Zoom level
+        get_center: If True, returns the center point coordinates. If False, returns the top-left corner.
+        
+    Returns:
+        Tuple of (latitude, longitude)
+    """
+    n = 2.0 ** z
+    if get_center:
+        x += 0.5
+        y += 0.5
+    
+    lon_deg = x / n * 360.0 - 180.0
+    lat_rad = math.atan(math.sinh(math.pi * (1 - 2 * y / n)))
+    lat_deg = math.degrees(lat_rad)
+    return lat_deg, lon_deg
+

tools/pretraining_data_builder/rsi_download/exceptions.py

@@ -0,0 +1,16 @@
+class InvalidWktPointArgument(Exception):
+    """Raised when the WKT string is not a valid point"""
+
+    pass
+
+
+class InvalidDateRangeArgument(Exception):
+    """Raised when the daterange string is not valid"""
+
+    pass
+
+
+class SearchException(Exception):
+    """Raised when search endpoint returned a non 200 statuscode"""
+
+    pass

tools/pretraining_data_builder/rsi_download/geo/geo_types.py

@@ -0,0 +1,13 @@
+from typing import List
+
+import msgspec
+
+
+class Coordinate(msgspec.Struct):
+    long: float
+    lat: float
+
+
+class GeoJsonPolygon(msgspec.Struct):
+    type: str
+    coordinates: List[List[List[float]]]

tools/pretraining_data_builder/rsi_download/sort.py

@@ -0,0 +1,12 @@
+def sort_by_cloudcover(search_result):
+    entries = search_result["feed"]["entry"]
+    sorted_entries = []
+    for entry in entries:
+        sorted_entries.append(
+            [
+                float(e["content"])
+                for e in entry["double"]
+                if e["name"] == "cloudcoverpercentage"
+            ][0]
+        )
+    return sorted(sorted_entries, key=float)

tools/pretraining_data_builder/rsi_pipeline/data_builder.py

@@ -0,0 +1,70 @@
+import lmdb
+import os
+import json
+from rich import print
+from rsi_download.download_async import download_core
+from rsi_process.adapter import process_adapter
+import asyncclick as click
+
+@click.command()
+@click.argument("lmdb_path", type=click.STRING)
+async def read_lmdb_file(lmdb_path):
+    """
+    Read the LMDB file and print all key-value pairs
+    
+    Args:
+        lmdb_path: LMDB file path
+    """
+    if not os.path.exists(lmdb_path):
+        print(f"Error: LMDB path '{lmdb_path}' does not exist")
+        return
+        
+    try:
+        print(f"Reading Pretraining List from LMDB file from {lmdb_path}...")
+        env = lmdb.open(lmdb_path, readonly=True)
+        total_length = 0
+        with env.begin() as txn:
+            key = b'length'
+            total_length = int(txn.get(key))
+            print(f"Total length of the Pretraining Data: {total_length:,}")
+            print("Example Data:")
+            for i in range(10):
+                print(txn.get(f"{i}".encode()).decode('utf-8'))
+            for i in range(total_length):
+                key = f"{i}".encode()
+                data = json.loads(txn.get(key).decode('utf-8'))
+                print("*"* 116 + "\n" + f"* Current Data [{i+1} / {total_length}]: {data} *" + "\n" + "*"* 116 )
+                print(f"Downloading: {data}")
+                await download_core(
+                    x=data['x'],
+                    y=data['y'],
+                    z=data['z'],
+                    date_min=data['date_min'],
+                    date_max=data['date_max'],
+                    username=os.getenv("USERNAME"),
+                    password=os.getenv("PASSWORD"),
+                    cloud_coverage=20.0,
+                    tci=False
+                )    
+                print('-'* 40)
+            print(f"Processing: {data}")
+            process_list = os.listdir('out_raw/')
+            total_len_process = len(process_list)
+            for fn in process_list:
+                print(f"Processing: {fn} [{i+1} / {total_len_process}]...")
+                process_adapter(
+                    fn_img=f'out_raw/{fn}',
+                    save_dir='out_processed/',
+                    verbose=True,
+                    use_gcj02=False
+                )
+            print('-'* 40)
+            print("Done!")
+            
+    except lmdb.Error as e:
+        print(f"Error reading LMDB file: {str(e)}")
+    finally:
+        env.close()
+
+if __name__ == "__main__":
+    read_lmdb_file()

tools/pretraining_data_builder/rsi_process/__main__.py

@@ -0,0 +1,19 @@
+import argparse
+from rsi_process.adapter import process_adapter
+
+def get_main_parser():
+    parser = argparse.ArgumentParser(description='RSI Processing Pipeline')
+    parser.add_argument('--fn_img', help='input zip file')
+    parser.add_argument('--save_dir', default='output/', help='prefix on oss bucket')
+    parser.add_argument('--verbose', action='store_true', default=True, help='whether to print info')
+    parser.add_argument('--use_gcj02', action='store_true', default=False, help='whether to use GCJ02 coordinate system')
+    return parser
+
+
+def main():
+    parser = get_main_parser()
+    args = parser.parse_args()
+    process_adapter(args.fn_img, args.save_dir, args.verbose, args.use_gcj02)
+
+if __name__ == '__main__':
+    main()

tools/pretraining_data_builder/rsi_process/adapter.py

@@ -0,0 +1,21 @@
+from rsi_process.script_s1_tiles import process_s1
+from rsi_process.script_s2_tiles import process_s2
+from rsi_process.script_wv_tiles import process_wv
+import EasyDict as edict
+
+
+def process_adapter(fn_img, save_dir, verbose, use_gcj02):
+    satellite_info = fn_img.split('/')[-1].split('_')[0]
+    if 'S2' in satellite_info:
+        satellite = 'S2'
+    elif 'S1' in satellite_info:
+        satellite = 'S1'
+    elif 'WV' in satellite_info:
+        satellite = 'WV'
+    args = edict(fn_img=fn_img, save_dir=save_dir, verbose=verbose, use_gcj02=use_gcj02)
+    if satellite == 'S1':
+        process_s1(args)
+    elif satellite == 'S2':
+        process_s2(args)
+    elif satellite == 'WV':
+        process_wv(args)

tools/pretraining_data_builder/rsi_process/script_s1_tiles.py

@@ -0,0 +1,238 @@
+import os
+import uuid
+import numpy as np
+import pyproj as prj
+from osgeo import gdal
+from time import time
+import mercantile
+from PIL import Image
+import utils_s1
+import imageio.v2 as iio
+from tile_resample import (
+    get_tile_array,
+    transfer
+)
+
+import argparse
+from rich import print
+from rich.progress import track
+
+def get_args_parser():
+    parser = argparse.ArgumentParser(description='Sentinel-1 to GCJ02 tiles')
+    parser.add_argument('--fn_img', help='input zip file of Sentinel-1 L1C')
+    parser.add_argument('--save_dir', default='output_s1/', help='prefix on oss bucket')
+    parser.add_argument('--verbose', action='store_true', default=True, help='whether to print info')
+    parser.add_argument('--use_gcj02', action='store_true', default=False, help='whether to use GCJ02 coordinate system')
+    return parser
+
+def process_s1(args):
+    t_start = time()
+    fn_img = args.fn_img
+    max_target_file = fn_img.split('_')[2][0:8]
+    verbose = args.verbose
+    save_rgb = True
+    nodata = 0
+
+    save_dir = args.save_dir
+    os.makedirs(save_dir, exist_ok=True)
+    thumb_save_dir = os.path.join(save_dir, 'thumb')
+    os.makedirs(thumb_save_dir, exist_ok=True)
+
+    print(f"converting {fn_img}...")
+
+    z = 14
+    bands = ['VV', 'VH']
+    buf = 1
+
+    def get_image_by_approximate_boundary(boundary):
+        '''
+        boundary: iterable of (lng, lat) in wgs84
+        '''
+        arr_lnglat = np.array(boundary)
+        xx, yy = tr_from_4326.transform(arr_lnglat[:, 0], arr_lnglat[:, 1])
+        row_min = int((tr[3] - yy.max()) / yres)
+        row_max = int((tr[3] - yy.min()) / yres)
+        col_min = int((xx.min() - tr[0]) / xres)
+        col_max = int((xx.max() - tr[0]) / xres)
+        row_min = max(0, row_min - buf)
+        row_max = min(ny - 1, row_max + buf)
+        col_min = max(0, col_min - buf)
+        col_max = min(nx - 1, col_max + buf)
+        if row_min > row_max or col_min > col_max:
+            return None
+        
+        arr_image = np.stack([
+            ds.ReadAsArray(col_min, row_min, col_max - col_min + 1, row_max - row_min + 1)
+            for ds in list_arr
+        ])
+
+        for iband in range(arr_image.shape[0]):
+            if np.any(arr_image[iband] != nodata):
+                break
+        else:
+            return None
+        arr_image = arr_image.transpose((1, 2, 0))
+        if arr_image.shape[2] == 1:
+            arr_image = arr_image[:, :, 0]
+        arr_xx = tr[0] + np.arange(col_min, col_max + 1) * xres
+        arr_yy = tr[3] - np.arange(row_min, row_max + 1) * yres
+        arr_xx, arr_yy = np.meshgrid(arr_xx, arr_yy)
+        arr_lngs, arr_lats = tr_to_4326.transform(arr_xx, arr_yy)
+        return arr_image, arr_lngs, arr_lats
+
+
+    rec = utils_s1.zip2rec(fn_img)
+    # import pdb; pdb.set_trace()
+    os.makedirs(os.path.join(thumb_save_dir, rec['sensing_start'].replace('-', '')), exist_ok=True)
+    thumb_save_path = os.path.join(thumb_save_dir, rec['sensing_start'].replace('-', ''), rec['product_uri'].replace('SAFE', 'png'))
+    iio.imwrite(thumb_save_path, rec['thumb'])
+
+    list_arr = []
+    for band in bands:
+        fn_jp2 = utils_s1.make_full_name(rec, band=band)
+        # import pdb; pdb.set_trace()
+        fn_jp2 = '/vsizip/' + os.path.join(fn_img, fn_jp2)
+        ds = gdal.Open(fn_jp2)
+        list_arr.append(ds)
+        if band == bands[0]:
+            nx, ny = ds.RasterXSize, ds.RasterYSize
+            if verbose: print('input size:', nx, ny)
+            tr = ds.GetGeoTransform()
+            if verbose:
+                print(gdal.Info(ds, format='json'))
+            # import pdb; pdb.set_trace()
+            try:
+                proj_wkt = ds.GetProjectionRef()
+                if proj_wkt:
+                    srs = prj.CRS.from_wkt(proj_wkt)
+                    epsg = int(srs.to_epsg())
+                else:
+                    proj_wkt = ds.GetGCPProjection()
+                    if proj_wkt:
+                        srs = prj.CRS.from_wkt(proj_wkt)
+                        epsg = int(srs.to_epsg())
+                    else:
+                        print("Warning: No projection information found, using default value 4326 (WGS84)")
+                        epsg = 4326
+            except Exception as e:
+                print(f"Warning: Unable to get EPSG code, using default value 4326 (WGS84). Error: {e}")
+                epsg = 4326
+
+            if verbose:
+                print(f"Used EPSG code: {epsg}")
+
+    size_pixel = mercantile.CE / 2 ** z / 256
+    radius = np.ceil(max(tr[1], -tr[5]) / size_pixel * 1.5)
+
+    buf_ext = buf
+    xmin = tr[0] - buf_ext * tr[1]
+    ymin = tr[3] + (ny + buf_ext) * tr[5]
+    xmax = tr[0] + (nx + buf_ext) * tr[1]
+    ymax = tr[3] - buf_ext * tr[5]
+    xres = tr[1]
+    yres = - tr[5]
+    if verbose:
+        print(
+            f'input extent, WGS84, buffered by {buf_ext} pixels: {xmin}, {ymin}, {xmax}, {ymax}'
+        )
+
+    tr_to_4326 = prj.Transformer.from_crs(epsg, 4326, always_xy=True)
+    tr_from_4326 = prj.Transformer.from_crs(4326, epsg, always_xy=True)
+    arr_lng, arr_lat = tr_to_4326.transform(
+        np.array([xmin, xmin, xmax, xmax]),
+        np.array([ymax, ymin, ymin, ymax])
+    )
+    # import pdb; pdb.set_trace()
+    if args.use_gcj02:
+        arr_lng_final, arr_lat_final = transfer.WGS84_to_GCJ02(arr_lng, arr_lat)
+    else:
+        arr_lng_final, arr_lat_final = arr_lng, arr_lat
+        
+    box = (
+        arr_lng_final.min(),
+        arr_lat_final.min(),
+        arr_lng_final.max(),
+        arr_lat_final.max()
+    )
+
+    if verbose:
+        coord_system = "GCJ02" if args.use_gcj02 else "WGS84"
+        print(f'input extent, {coord_system}: {box}')
+
+    tile_ul = mercantile.tile(box[0], box[3], z)
+    tile_lr = mercantile.tile(box[2], box[1], z)
+
+    if verbose:
+        print('Upperleft  ', str(tile_ul))
+        print('Lowerright ', str(tile_lr))
+
+    def work(x, y, z, save_rgb):
+        arr_tile = get_tile_array(
+            x, y, z,
+            method='nearest',
+            func_source=get_image_by_approximate_boundary,
+            radius=radius,
+            use_gc02=args.use_gcj02
+        )
+        y_str = str(y)
+        if arr_tile is not None:
+            indi_gap = arr_tile[:, :, 0] == 0
+
+            dict_arr = {
+                band: arr_tile[:, :, i_band]
+                for i_band, band in enumerate(bands)
+            }
+            save_path = os.path.join(save_dir, str(z), str(x))
+            os.makedirs(save_path, exist_ok=True)
+            
+            npz_filename = os.path.join(save_path, f'{y_str}_{max_target_file}.npz')
+            
+            if indi_gap.any():
+                if os.path.exists(npz_filename):
+                    try:
+                        fp = np.load(npz_filename)
+                        for band in bands:
+                            dict_arr[band][indi_gap] = fp[band][indi_gap]
+                        
+                    except Exception as e:
+                        print(e)
+                        print("datasize is 0", npz_filename)
+                        pass
+            
+            np.savez_compressed(npz_filename, **dict_arr)
+            if verbose:
+                print(f"npz file for X={str(x)}, Y={y_str}, Z={str(z)} date={max_target_file} generated!")
+            if save_rgb:
+                arr_rgb = np.stack([dict_arr['B4'], dict_arr['B3'], dict_arr['B2']], axis=-1)
+                arr_rgb = np.clip(arr_rgb / 3000. * 255, 0, 255).astype(np.uint8)
+                image_tile = Image.fromarray(arr_rgb)
+                
+                png_filename = os.path.join(save_path, f'{y_str}_{max_target_file}.png')
+                image_tile.save(png_filename, format='png')
+
+    diff_list = []
+
+    tasks = [
+        (x, y) for x in range(tile_ul.x, tile_lr.x + 1)
+        for y in range(tile_ul.y, tile_lr.y + 1)
+    ]
+    
+    for x, y in track(tasks, description="converting tiles..."):
+        work(x, y, z, save_rgb)
+        diff_list.append(os.path.join(str(z), str(x), f'{y}_{max_target_file}.npz'))
+
+    diff_path = os.path.join(save_dir, 'diff', 'new')
+    os.makedirs(diff_path, exist_ok=True)
+    diff_filename = os.path.join(diff_path, f"{z}-{os.path.splitext(os.path.basename(fn_img))[0]}-{uuid.uuid1()}.txt")
+    with open(diff_filename, 'w') as f:
+        f.write('\n'.join(diff_list))
+
+    print("time cost :", time() - t_start)
+
+def main():
+    args = get_args_parser().parse_args()
+    process_s1(args)
+
+if __name__ == '__main__':
+    main()
+

tools/pretraining_data_builder/rsi_process/script_s2_tiles.py

@@ -0,0 +1,226 @@
+import os
+import uuid
+import numpy as np
+import pyproj as prj
+from osgeo import gdal
+from time import time
+import mercantile
+from PIL import Image
+import utils_s2
+import imageio.v2 as iio
+from tile_resample import (
+    get_tile_array,
+    transfer
+)
+
+import argparse
+from rich import print
+from rich.progress import track
+
+def get_args_parser():
+    parser = argparse.ArgumentParser(description='Sentinel-2 to GCJ02 tiles')
+    parser.add_argument('--fn_img', help='input zip file of Sentinel-2 L2A') # /Users/wukang/Projects/sentinel2-downloader/S2A_MSIL2A_20220615T024601_N0400_R132_T50SNA_20220615T062308.zip
+    parser.add_argument('--resolution', type=int, help='10 or 20 meter resolution bands')
+    parser.add_argument('--save_dir', default='output_s2/', help='prefix on oss bucket')
+    parser.add_argument('--verbose', action='store_true', default=True, help='whether to print info')
+    parser.add_argument('--use_gcj02', action='store_true', default=False, help='whether to use GCJ02 coordinate system')
+    return parser.parse_args()
+
+def process_s2(args):
+    t_start = time()
+    fn_img = args.fn_img
+    max_target_file = fn_img.split('_')[2][0:8]
+    resolution = args.resolution
+    verbose = args.verbose
+    save_rgb = True
+    nodata = 0
+
+    save_dir = args.save_dir
+    os.makedirs(save_dir, exist_ok=True)
+    thumb_save_dir = os.path.join(save_dir, 'thumb')
+    os.makedirs(thumb_save_dir, exist_ok=True)
+
+    print(f"converting {fn_img}...")
+    if resolution == 10:
+        z = 14
+        bands = ['B2', 'B3', 'B4', 'B8']
+        buf = 1
+    elif resolution == 20:
+        z = 13
+        bands = ['B5', 'B6', 'B7', 'B8A', 'B11', 'B12', 'SCL']
+        buf = 1
+        save_rgb = False
+    else:
+        raise Exception(f'Unknown resoluiton: {resolution}')
+
+
+    def get_image_by_approximate_boundary(boundary):
+        '''
+        boundary: iterable of (lng, lat) in wgs84
+        '''
+        arr_lnglat = np.array(boundary)
+        xx, yy = tr_from_4326.transform(arr_lnglat[:, 0], arr_lnglat[:, 1])
+        row_min = int((tr[3] - yy.max()) / yres)
+        row_max = int((tr[3] - yy.min()) / yres)
+        col_min = int((xx.min() - tr[0]) / xres)
+        col_max = int((xx.max() - tr[0]) / xres)
+        row_min = max(0, row_min - buf)
+        row_max = min(ny - 1, row_max + buf)
+        col_min = max(0, col_min - buf)
+        col_max = min(nx - 1, col_max + buf)
+        if row_min > row_max or col_min > col_max:
+            return None
+        
+        arr_image = np.stack([
+            ds.ReadAsArray(col_min, row_min, col_max - col_min + 1, row_max - row_min + 1)
+            for ds in list_arr
+        ])
+
+        for iband in range(arr_image.shape[0]):
+            if np.any(arr_image[iband] != nodata):
+                break
+        else:
+            return None
+        arr_image = arr_image.transpose((1, 2, 0))
+        if arr_image.shape[2] == 1:
+            arr_image = arr_image[:, :, 0]
+        arr_xx = tr[0] + np.arange(col_min, col_max + 1) * xres
+        arr_yy = tr[3] - np.arange(row_min, row_max + 1) * yres
+        arr_xx, arr_yy = np.meshgrid(arr_xx, arr_yy)
+        arr_lngs, arr_lats = tr_to_4326.transform(arr_xx, arr_yy)
+        return arr_image, arr_lngs, arr_lats
+
+
+    rec = utils_s2.zip2rec(fn_img)
+
+    os.makedirs(os.path.join(thumb_save_dir, rec['sensing_start'].replace('-', '')), exist_ok=True)
+    thumb_save_path = os.path.join(thumb_save_dir, rec['sensing_start'].replace('-', ''), rec['product_uri'].replace('SAFE', 'png'))
+    iio.imwrite(thumb_save_path, rec['thumb'])
+
+    list_arr = []
+    for band in bands:
+        fn_jp2 = utils_s2.make_full_name(rec, band=band)
+        fn_jp2 = '/vsizip/' + os.path.join(fn_img, fn_jp2)
+        ds = gdal.Open(fn_jp2)
+        list_arr.append(ds)
+        if band == bands[0]:
+            nx, ny = ds.RasterXSize, ds.RasterYSize
+            if verbose: print('input size:', nx, ny)
+            tr = ds.GetGeoTransform()
+            if verbose:
+                print(gdal.Info(ds, format='json'))
+            epsg = int(
+                gdal.Info(ds, format='json')['coordinateSystem']['wkt'].rsplit('"EPSG",', 1)[-1][:-2]
+            )
+
+    size_pixel = mercantile.CE / 2 ** z / 256
+    radius = np.ceil(max(tr[1], -tr[5]) / size_pixel * 1.5)
+
+    buf_ext = buf
+    xmin = tr[0] - buf_ext * tr[1]
+    ymin = tr[3] + (ny + buf_ext) * tr[5]
+    xmax = tr[0] + (nx + buf_ext) * tr[1]
+    ymax = tr[3] - buf_ext * tr[5]
+    xres = tr[1]
+    yres = - tr[5]
+    if verbose:
+        print(
+            f'input extent, WGS84, buffered by {buf_ext} pixels: {xmin}, {ymin}, {xmax}, {ymax}'
+        )
+
+    tr_to_4326 = prj.Transformer.from_crs(epsg, 4326, always_xy=True)
+    tr_from_4326 = prj.Transformer.from_crs(4326, epsg, always_xy=True)
+    arr_lng, arr_lat = tr_to_4326.transform(
+        np.array([xmin, xmin, xmax, xmax]),
+        np.array([ymax, ymin, ymin, ymax])
+    )
+    
+    if args.use_gcj02:
+        arr_lng_final, arr_lat_final = transfer.WGS84_to_GCJ02(arr_lng, arr_lat)
+    else:
+        arr_lng_final, arr_lat_final = arr_lng, arr_lat
+        
+    box = (
+        arr_lng_final.min(),
+        arr_lat_final.min(),
+        arr_lng_final.max(),
+        arr_lat_final.max()
+    )
+
+    if verbose:
+        coord_system = "GCJ02" if args.use_gcj02 else "WGS84"
+        print(f'input extent, {coord_system}: {box}')
+
+    tile_ul = mercantile.tile(box[0], box[3], z)
+    tile_lr = mercantile.tile(box[2], box[1], z)
+
+    if verbose:
+        print('Upperleft  ', str(tile_ul))
+        print('Lowerright ', str(tile_lr))
+
+    def work(x, y, z, save_rgb):
+        arr_tile = get_tile_array(
+            x, y, z,
+            method='nearest',
+            func_source=get_image_by_approximate_boundary,
+            radius=radius,
+            use_gc02=args.use_gcj02
+        )
+        y_str = str(y)
+        if arr_tile is not None:
+            indi_gap = arr_tile[:, :, 0] == 0
+
+            dict_arr = {
+                band: arr_tile[:, :, i_band]
+                for i_band, band in enumerate(bands)
+            }
+            save_path = os.path.join(save_dir, str(z), str(x))
+            os.makedirs(save_path, exist_ok=True)
+            
+            npz_filename = os.path.join(save_path, f'{y_str}_{max_target_file}.npz')
+            
+            if indi_gap.any():
+                if os.path.exists(npz_filename):
+                    try:
+                        fp = np.load(npz_filename)
+                        for band in bands:
+                            dict_arr[band][indi_gap] = fp[band][indi_gap]
+                        
+                    except Exception as e:
+                        print(e)
+                        print("datasize is 0", npz_filename)
+                        pass
+            
+            np.savez_compressed(npz_filename, **dict_arr)
+            if verbose:
+                print(f"npz file for X={str(x)}, Y={y_str}, Z={str(z)} date={max_target_file} generated!")
+            if save_rgb:
+                arr_rgb = np.stack([dict_arr['B4'], dict_arr['B3'], dict_arr['B2']], axis=-1)
+                arr_rgb = np.clip(arr_rgb / 3000. * 255, 0, 255).astype(np.uint8)
+                image_tile = Image.fromarray(arr_rgb)
+                
+                png_filename = os.path.join(save_path, f'{y_str}_{max_target_file}.png')
+                image_tile.save(png_filename, format='png')
+
+    diff_list = []
+
+    tasks = [
+        (x, y) for x in range(tile_ul.x, tile_lr.x + 1)
+        for y in range(tile_ul.y, tile_lr.y + 1)
+    ]
+    
+    for x, y in track(tasks, description="converting tiles..."):
+        work(x, y, z, save_rgb)
+        diff_list.append(os.path.join(str(z), str(x), f'{y}_{max_target_file}.npz'))
+
+    diff_path = os.path.join(save_dir, 'diff', 'new')
+    os.makedirs(diff_path, exist_ok=True)
+    diff_filename = os.path.join(diff_path, f"{z}-{os.path.splitext(os.path.basename(fn_img))[0]}-{uuid.uuid1()}.txt")
+    with open(diff_filename, 'w') as f:
+        f.write('\n'.join(diff_list))
+
+    print("time cost :", time() - t_start)
+
+if __name__ == '__main__':
+    args = get_args_parser()
+    process_s2(args)

tools/pretraining_data_builder/rsi_process/script_wv_tiles.py

@@ -0,0 +1,183 @@
+import os
+import uuid
+import numpy as np
+import pyproj as prj
+from osgeo import gdal
+from time import time
+import mercantile
+from PIL import Image
+import imageio.v2 as iio
+from tile_resample import (
+    get_tile_array,
+    transfer
+)
+import argparse
+from rich import print
+from rich.progress import track
+
+def get_args_parser():
+    parser = argparse.ArgumentParser(description='WorldView to tiles')
+    parser.add_argument('--fn_img', help='input file of WorldView image')
+    parser.add_argument('--save_dir', default='output_wv/', help='output directory')
+    parser.add_argument('--zoom', type=int, default=16, help='zoom level')
+    parser.add_argument('--verbose', action='store_true', default=True)
+    parser.add_argument('--use_gcj02', action='store_true', default=False)
+    return parser.parse_args()
+
+def get_image_by_approximate_boundary(ds_list, boundary, tr, buf=1):
+    '''Get image data within a specified boundary
+    
+    Args:
+        ds_list: List of GDAL datasets
+        boundary: List of (lng, lat) coordinates
+        tr: Geotransformation parameters
+        buf: Buffer size
+    '''
+    arr_lnglat = np.array(boundary)
+    tr_from_4326 = prj.Transformer.from_crs(4326, ds_list[0].GetProjection(), always_xy=True)
+    
+    xx, yy = tr_from_4326.transform(arr_lnglat[:, 0], arr_lnglat[:, 1])
+    
+    nx = ds_list[0].RasterXSize
+    ny = ds_list[0].RasterYSize
+    xres = tr[1]
+    yres = -tr[5]
+    
+    row_min = int((tr[3] - yy.max()) / yres)
+    row_max = int((tr[3] - yy.min()) / yres)
+    col_min = int((xx.min() - tr[0]) / xres)
+    col_max = int((xx.max() - tr[0]) / xres)
+    
+    row_min = max(0, row_min - buf)
+    row_max = min(ny - 1, row_max + buf)
+    col_min = max(0, col_min - buf)
+    col_max = min(nx - 1, col_max + buf)
+    
+    if row_min > row_max or col_min > col_max:
+        return None
+    
+    arr_image = np.stack([
+        ds.ReadAsArray(col_min, row_min, col_max - col_min + 1, row_max - row_min + 1)
+        for ds in ds_list
+    ])
+    
+    if np.all(arr_image == 0):
+        return None
+        
+    arr_image = arr_image.transpose((1, 2, 0))
+    
+    arr_xx = tr[0] + np.arange(col_min, col_max + 1) * xres
+    arr_yy = tr[3] - np.arange(row_min, row_max + 1) * yres
+    arr_xx, arr_yy = np.meshgrid(arr_xx, arr_yy)
+    
+    tr_to_4326 = prj.Transformer.from_crs(ds_list[0].GetProjection(), 4326, always_xy=True)
+    arr_lngs, arr_lats = tr_to_4326.transform(arr_xx, arr_yy)
+    
+    return arr_image, arr_lngs, arr_lats
+
+def process_wv(args):
+    t_start = time()
+    
+    fn_img = args.fn_img
+    save_dir = args.save_dir
+    z = args.zoom
+    verbose = args.verbose
+    
+    os.makedirs(save_dir, exist_ok=True)
+    
+    ds = gdal.Open(fn_img)
+    if ds is None:
+        raise Exception(f"Cannot open {fn_img}")
+        
+    bands = [ds.GetRasterBand(i+1) for i in range(ds.RasterCount)]
+    list_arr = [ds]
+    
+    nx, ny = ds.RasterXSize, ds.RasterYSize
+    tr = ds.GetGeoTransform()
+    
+    if verbose:
+        print('Input size:', nx, ny)
+        print(gdal.Info(ds, format='json'))
+    
+    # Calculate the image range
+    size_pixel = mercantile.CE / 2 ** z / 256
+    radius = np.ceil(max(tr[1], -tr[5]) / size_pixel * 1.5)
+    
+    buf_ext = 1
+    xmin = tr[0] - buf_ext * tr[1]
+    ymin = tr[3] + (ny + buf_ext) * tr[5]
+    xmax = tr[0] + (nx + buf_ext) * tr[1]
+    ymax = tr[3] - buf_ext * tr[5]
+    
+    tr_to_4326 = prj.Transformer.from_crs(ds.GetProjection(), 4326, always_xy=True)
+    arr_lng, arr_lat = tr_to_4326.transform(
+        np.array([xmin, xmin, xmax, xmax]),
+        np.array([ymax, ymin, ymin, ymax])
+    )
+    
+    if args.use_gcj02:
+        arr_lng_final, arr_lat_final = transfer.WGS84_to_GCJ02(arr_lng, arr_lat)
+    else:
+        arr_lng_final, arr_lat_final = arr_lng, arr_lat
+        
+    box = (
+        arr_lng_final.min(),
+        arr_lat_final.min(),
+        arr_lng_final.max(),
+        arr_lat_final.max()
+    )
+    
+    if verbose:
+        coord_system = "GCJ02" if args.use_gcj02 else "WGS84"
+        print(f'Input extent, {coord_system}: {box}')
+    
+    # Calculate the tile range to be processed
+    tile_ul = mercantile.tile(box[0], box[3], z)
+    tile_lr = mercantile.tile(box[2], box[1], z)
+    
+    if verbose:
+        print('Upperleft  ', str(tile_ul))
+        print('Lowerright ', str(tile_lr))
+    
+    def work(x, y, z):
+        arr_tile = get_tile_array(
+            x, y, z,
+            method='nearest',
+            func_source=lambda boundary: get_image_by_approximate_boundary(list_arr, boundary, tr),
+            radius=radius,
+            use_gc02=args.use_gcj02
+        )
+        
+        if arr_tile is not None:
+            save_path = os.path.join(save_dir, str(z), str(x))
+            os.makedirs(save_path, exist_ok=True)
+            
+            # Save as PNG
+            if arr_tile.shape[2] >= 3:
+                arr_rgb = arr_tile[:, :, :3]
+                arr_rgb = np.clip(arr_rgb / 2000. * 255, 0, 255).astype(np.uint8)
+                image_tile = Image.fromarray(arr_rgb)
+                png_filename = os.path.join(save_path, f'{y}.png')
+                image_tile.save(png_filename, format='png')
+            
+            # Save as NPZ
+            dict_arr = {f'B{i+1}': arr_tile[:, :, i] for i in range(arr_tile.shape[2])}
+            npz_filename = os.path.join(save_path, f'{y}.npz')
+            np.savez_compressed(npz_filename, **dict_arr)
+    
+    tasks = [
+        (x, y) for x in range(tile_ul.x, tile_lr.x + 1)
+        for y in range(tile_ul.y, tile_lr.y + 1)
+    ]
+    
+    for x, y in track(tasks, description="Converting tiles..."):
+        work(x, y, z)
+    
+    print("Time cost:", time() - t_start)
+
+def main():
+    args = get_args_parser()
+    process_wv(args)
+
+if __name__ == '__main__':
+    main()

tools/pretraining_data_builder/rsi_process/tile_resample.py

@@ -0,0 +1,230 @@
+import numpy as np
+import mercantile
+from pyresample import bilinear, kd_tree, geometry
+
+TILE_SIZE = 256
+
+class LngLatTransfer():
+
+    def __init__(self):
+        self.x_pi = 3.14159265358979324 * 3000.0 / 180.0
+        self.pi = np.pi  # π
+        self.a = 6378245.0 
+        self.es = 0.00669342162296594323  
+        pass
+
+    def GCJ02_to_BD09(self, gcj_lng, gcj_lat):
+        """
+        Convert coordinates from GCJ02 to BD09 coordinate system
+        :param lng: Longitude in GCJ02 coordinate system
+        :param lat: Latitude in GCJ02 coordinate system
+        :return: Converted longitude and latitude in BD09
+        """
+        z = np.sqrt(gcj_lng * gcj_lng + gcj_lat * gcj_lat) + 0.00002 * np.sin(gcj_lat * self.x_pi)
+        theta = np.arctan2(gcj_lat, gcj_lng) + 0.000003 * np.cos(gcj_lng * self.x_pi)
+        bd_lng = z * np.cos(theta) + 0.0065
+        bd_lat = z * np.sin(theta) + 0.006
+        return bd_lng, bd_lat
+
+
+    def BD09_to_GCJ02(self, bd_lng, bd_lat):
+        '''
+        Convert coordinates from BD09 to GCJ02 coordinate system
+        :param bd_lng: Longitude in BD09 coordinate system
+        :param bd_lat: Latitude in BD09 coordinate system
+        :return: Converted longitude and latitude in GCJ02
+        '''
+        x = bd_lng - 0.0065
+        y = bd_lat - 0.006
+        z = np.sqrt(x * x + y * y) - 0.00002 * np.sin(y * self.x_pi)
+        theta = np.arctan2(y, x) - 0.000003 * np.cos(x * self.x_pi)
+        gcj_lng = z * np.cos(theta)
+        gcj_lat = z * np.sin(theta)
+        return gcj_lng, gcj_lat
+
+
+    def WGS84_to_GCJ02(self, lng, lat):
+        '''
+        Convert coordinates from WGS84 to GCJ02 coordinate system
+        :param lng: Longitude in WGS84 coordinate system
+        :param lat: Latitude in WGS84 coordinate system
+        :return: Converted longitude and latitude in GCJ02
+        '''
+        dlat = self._transformlat(lng - 105.0, lat - 35.0)
+        dlng = self._transformlng(lng - 105.0, lat - 35.0)
+        radlat = lat / 180.0 * self.pi
+        magic = np.sin(radlat)
+        magic = 1 - self.es * magic * magic
+        sqrtmagic = np.sqrt(magic)
+        dlat = (dlat * 180.0) / ((self.a * (1 - self.es)) / (magic * sqrtmagic) * self.pi)
+        dlng = (dlng * 180.0) / (self.a / sqrtmagic * np.cos(radlat) * self.pi)
+        gcj_lng = lng + dlng
+        gcj_lat = lat + dlat
+        return gcj_lng, gcj_lat
+
+
+    def GCJ02_to_WGS84(self, gcj_lng, gcj_lat):
+        '''
+        Convert coordinates from GCJ02 to WGS84 coordinate system
+        :param gcj_lng: Longitude in GCJ02 coordinate system
+        :param gcj_lat: Latitude in GCJ02 coordinate system
+        :return: Converted longitude and latitude in WGS84
+        '''
+        dlat = self._transformlat(gcj_lng - 105.0, gcj_lat - 35.0)
+        dlng = self._transformlng(gcj_lng - 105.0, gcj_lat - 35.0)
+        radlat = gcj_lat / 180.0 * self.pi
+        magic = np.sin(radlat)
+        magic = 1 - self.es * magic * magic
+        sqrtmagic = np.sqrt(magic)
+        dlat = (dlat * 180.0) / ((self.a * (1 - self.es)) / (magic * sqrtmagic) * self.pi)
+        dlng = (dlng * 180.0) / (self.a / sqrtmagic * np.cos(radlat) * self.pi)
+        mglat = gcj_lat + dlat
+        mglng = gcj_lng + dlng
+        lng = gcj_lng * 2 - mglng
+        lat = gcj_lat * 2 - mglat
+        return lng, lat
+
+
+    def BD09_to_WGS84(self, bd_lng, bd_lat):
+        '''
+        Convert coordinates from BD09 to WGS84 coordinate system
+        :param bd_lng: Longitude in BD09 coordinate system
+        :param bd_lat: Latitude in BD09 coordinate system
+        :return: Converted longitude and latitude in WGS84
+        '''
+        lng, lat = self.BD09_to_GCJ02(bd_lng, bd_lat)
+        return self.GCJ02_to_WGS84(lng, lat)
+
+
+    def WGS84_to_BD09(self, lng, lat):
+        '''
+        Convert coordinates from WGS84 to BD09 coordinate system
+        :param lng: Longitude in WGS84 coordinate system
+        :param lat: Latitude in WGS84 coordinate system
+        :return: Converted longitude and latitude in BD09
+        '''
+        lng, lat = self.WGS84_to_GCJ02(lng, lat)
+        return self.GCJ02_to_BD09(lng, lat)
+
+
+    def _transformlat(self, lng, lat):
+        ret = -100.0 + 2.0 * lng + 3.0 * lat + 0.2 * lat * lat + \
+              0.1 * lng * lat + 0.2 * np.sqrt(np.fabs(lng))
+        ret += (20.0 * np.sin(6.0 * lng * self.pi) + 20.0 *
+                np.sin(2.0 * lng * self.pi)) * 2.0 / 3.0
+        ret += (20.0 * np.sin(lat * self.pi) + 40.0 *
+                np.sin(lat / 3.0 * self.pi)) * 2.0 / 3.0
+        ret += (160.0 * np.sin(lat / 12.0 * self.pi) + 320 *
+                np.sin(lat * self.pi / 30.0)) * 2.0 / 3.0
+        return ret
+
+
+    def _transformlng(self, lng, lat):
+        ret = 300.0 + lng + 2.0 * lat + 0.1 * lng * lng + \
+              0.1 * lng * lat + 0.1 * np.sqrt(np.fabs(lng))
+        ret += (20.0 * np.sin(6.0 * lng * self.pi) + 20.0 *
+                np.sin(2.0 * lng * self.pi)) * 2.0 / 3.0
+        ret += (20.0 * np.sin(lng * self.pi) + 40.0 *
+                np.sin(lng / 3.0 * self.pi)) * 2.0 / 3.0
+        ret += (150.0 * np.sin(lng / 12.0 * self.pi) + 300.0 *
+                np.sin(lng / 30.0 * self.pi)) * 2.0 / 3.0
+        return ret
+
+    def WGS84_to_WebMercator(self, lng, lat):
+        '''
+        Convert coordinates from WGS84 to Web Mercator
+        :param lng: Longitude in WGS84
+        :param lat: Latitude in WGS84
+        :return: Converted Web Mercator coordinates
+        '''
+        x = lng * 20037508.342789 / 180
+        y = np.log(np.tan((90 + lat) * self.pi / 360)) / (self.pi / 180)
+        y = y * 20037508.34789 / 180
+        return x, y
+
+    def WebMercator_to_WGS84(self, x, y):
+        '''
+        Convert coordinates from Web Mercator to WGS84
+        :param x: Web Mercator x coordinate
+        :param y: Web Mercator y coordinate
+        :return: Converted longitude and latitude in WGS84
+        '''
+        lng = x / 20037508.34 * 180
+        lat = y / 20037508.34 * 180
+        lat = 180 / self.pi * (2 * np.arctan(np.exp(lat * self.pi / 180)) - self.pi / 2)
+        return lng, lat
+
+
+transfer = LngLatTransfer()
+def get_tile_array(x, y, z, method='nearest', func_source=None, radius=2, fill_value=0, use_gc02=True):
+    """Resample source image data to map tile
+    
+    Args:
+        x, y, z: Tile coordinates
+        method: Resampling method ('nearest' or 'bilinear')
+        func_source: Function to get source image data
+        radius: Search radius in pixels
+        fill_value: Value for no data areas
+        gc02: Whether the coordinates are in GCJ02 system (True) or WGS84 (False)
+        
+    Returns:
+        ndarray: Resampled tile data
+    """
+    bounds = mercantile.bounds(x, y, z)
+    
+    if use_gc02:
+        # Convert coordinates from GCJ02 to WGS84
+        wgs84_lngs, wgs84_lats = transfer.GCJ02_to_WGS84(
+            gcj_lng=np.array([bounds.west, bounds.west, bounds.east, bounds.east]),
+            gcj_lat=np.array([bounds.north, bounds.south, bounds.south, bounds.north])
+        )
+        boundary = list(zip(wgs84_lngs, wgs84_lats))
+    else:
+        boundary = list(zip(
+            [bounds.west, bounds.west, bounds.east, bounds.east],
+            [bounds.north, bounds.south, bounds.south, bounds.north]
+        ))
+    
+    source_data = func_source(boundary)
+    
+    if source_data is None:
+        return None
+        
+    arr_image, arr_lngs, arr_lats = source_data
+    
+    if use_gc02:
+        gcj02_lngs, gcj02_lats = transfer.WGS84_to_GCJ02(arr_lngs, arr_lats)
+    else:
+        gcj02_lngs, gcj02_lats = arr_lngs, arr_lats
+    
+    # Define source and target geometries
+    source_def = geometry.SwathDefinition(lons=gcj02_lngs, lats=gcj02_lats)
+    
+    xy_bounds = mercantile.xy_bounds(x, y, z)
+    target_def = geometry.AreaDefinition(
+        'tile', 'tile', 'tile',
+        'EPSG:3857',
+        TILE_SIZE, TILE_SIZE,
+        (xy_bounds.left, xy_bounds.bottom, xy_bounds.right, xy_bounds.top)
+    )
+    
+    # Resample
+    pixel_size = mercantile.CE / 2 ** z / TILE_SIZE
+    if method == 'nearest':
+        result = kd_tree.resample_nearest(
+            source_def, arr_image, target_def,
+            radius_of_influence=radius * pixel_size,
+            fill_value=fill_value
+        )
+    elif method == 'bilinear':
+        resampler = bilinear.NumpyBilinearResampler(
+            source_def, target_def,
+            radius_of_influence=radius * pixel_size,
+            neighbours=8
+        )
+        result = resampler.resample(arr_image).astype(arr_image.dtype)
+    else:
+        raise ValueError(f'Unknown resampling method: {method}')
+        
+    return result
+

tools/pretraining_data_builder/rsi_process/utils_s1.py

@@ -0,0 +1,133 @@
+import xml.dom.minidom
+import os
+from glob import glob
+import zipfile
+from shapely import wkt
+import geopandas as gpd
+from osgeo import gdal
+import imageio.v2 as iio
+
+def parse_metadata(meta_xml_file):
+    """Parse Sentinel-1 metadata XML file
+    
+    Args:
+        meta_xml_file: Metadata XML file path
+        
+    Returns:
+        dict: Dictionary containing key metadata information
+    """
+    record = {}
+
+    dom = xml.dom.minidom.parse(meta_xml_file)    # Get sensing start time
+    sensing_start = dom.getElementsByTagName('startTime')[0].firstChild.data
+    
+    product_uri = meta_xml_file.name.split('/')[0]
+    
+    record.update({
+        'product_uri': product_uri,
+        'sensing_start': sensing_start,
+    })
+        
+        
+    return record
+
+def convert_footprint_to_wkt(footprint):
+    """Convert footprint string to WKT format"""
+    coords = footprint.strip().split(' ')
+    wkt_coords = []
+    for coord in coords:
+        lat, lon = coord.split(',')
+        wkt_coords.append(f"{lon} {lat}")
+    return f"MULTIPOLYGON ((({','.join(wkt_coords)})))"
+
+def zip2rec(fn_zip):
+    id_img = os.path.splitext(os.path.basename(fn_zip))[0]
+    archive = zipfile.ZipFile(fn_zip, 'r')
+    xml_files = [f for f in archive.namelist() if f.endswith('-001.xml')]
+    if not xml_files:
+        raise FileNotFoundError(f"No XML file ending with '-001.xml' found in {fn_zip}")
+    fn_xml = archive.open(xml_files[0])
+    rec = parse_metadata(fn_xml)
+    import pdb; pdb.set_trace()
+    # rec['geometry'] = wkt.loads(rec['geom_wkt'])
+    thumb = archive.open(os.path.join(f'{id_img}.SAFE', 'preview', 'quick-look.png'))
+    thumb = iio.imread(thumb)
+    rec['thumb'] = thumb
+    return rec
+
+def build_catalog(path, fn='catalog'):
+    '''
+    fn: filename or None
+    '''
+    list_fnames = glob(os.path.join(path, 'S2*.zip'))
+
+    list_rec = []
+    for fn_zip in list_fnames:
+        rec = zip2rec(fn_zip)
+        list_rec.append(rec)
+
+    gdf = gpd.GeoDataFrame(list_rec, crs='EPSG:4326').drop(columns='geom_wkt')
+    if fn is not None:
+        fn_geojson = os.path.join(path, f"{fn}.geojson")
+        gdf.to_file(fn_geojson, driver='GeoJSON')
+        return fn_geojson
+    else:
+        return gdf
+
+def make_full_name(rec, band):
+    dict_bands = {
+        'VV': '001',
+        'VH': '002',
+    }
+    parts = rec['product_uri'].split('_')
+    
+    satellite = parts[0].lower()  # S1A -> s1a
+    mode = parts[1].lower()       # IW -> iw
+    product_type = parts[2][:3].lower()  # GRDH -> grd
+    polarization = band.lower()         # Assume polarization mode is VV
+    start_time = parts[4].lower() # Start time
+    end_time = parts[5].lower()   # End time
+    id1 = parts[6].lower()        # 058175
+    id2 = parts[7].lower()        # 072FF2
+    fixed_part = dict_bands[band]          # Replace fixed part with 001
+    
+    # Concatenate to target format
+    file_name = f"{satellite}-{mode}-{product_type}-{polarization}-{start_time}-{end_time}-{id1}-{id2}-{fixed_part}.tiff"
+
+    fn_template = os.path.join(
+        rec['product_uri'], 'measurement', file_name
+    )
+    return fn_template
+
+def warp(
+    ds, outputBounds, 
+    outputBoundsSRS='EPSG:4326',
+    xRes=10, yRes=10, targetAlignedPixels=True,
+    **kwargs,
+):
+    options_warp = gdal.WarpOptions(
+        format="MEM",
+        outputBounds=outputBounds, 
+        outputBoundsSRS=outputBoundsSRS,
+        xRes=xRes, yRes=yRes, targetAlignedPixels=targetAlignedPixels,
+        **kwargs,
+    )
+    ds_warp = gdal.Warp('', ds, options=options_warp)
+    return ds_warp
+
+def get_ndarray(
+    ds, outputBounds, 
+    outputBoundsSRS='EPSG:4326',
+    xRes=10, yRes=10, targetAlignedPixels=True,
+    **kwargs,
+):
+    ds_warp = warp(
+        ds, outputBounds, 
+        outputBoundsSRS='EPSG:4326',
+        xRes=10, yRes=10, targetAlignedPixels=True,
+        **kwargs
+    )
+    arr = ds_warp.ReadAsArray()
+    ds_warp = None
+    return arr
+

tools/pretraining_data_builder/rsi_process/utils_s2.py

@@ -0,0 +1,158 @@
+import xml.dom.minidom
+import os
+from glob import glob
+import zipfile
+from shapely import wkt
+import geopandas as gpd
+from osgeo import gdal
+import imageio.v2 as iio
+
+def parse_metadata(meta_xml_file):
+    """Parse Sentinel-2 metadata XML file
+    
+    Args:
+        meta_xml_file: Path to metadata XML file
+        
+    Returns:
+        dict: Metadata information including sensing time, product URI, etc.
+    """
+    record = {}
+    try:
+        dom = xml.dom.minidom.parse(meta_xml_file)
+        
+        # Get sensing start time
+        sensing_start = dom.getElementsByTagName('DATATAKE_SENSING_START')[0].firstChild.data[0:10]
+        
+        # Get product URI and image paths
+        product_uri = dom.getElementsByTagName('PRODUCT_URI')[0].firstChild.data
+        
+        image_file = dom.getElementsByTagName('IMAGE_FILE')[0].firstChild.data
+        items = image_file.split('/')
+        granule_path = items[1]
+        img_name = items[4].split('_')[0] + '_' + items[4].split('_')[1]
+        
+        # Get footprint
+        footprint = dom.getElementsByTagName('EXT_POS_LIST')[0].firstChild.data
+        geom_wkt = convert_footprint_to_wkt(footprint)
+        
+        # Get cloud coverage info
+        cloud_coverage = float(dom.getElementsByTagName('Cloud_Coverage_Assessment')[0].firstChild.data)
+        cloud_shadow = float(dom.getElementsByTagName('CLOUD_SHADOW_PERCENTAGE')[0].firstChild.data)
+        medium_clouds = float(dom.getElementsByTagName('MEDIUM_PROBA_CLOUDS_PERCENTAGE')[0].firstChild.data)
+        high_clouds = float(dom.getElementsByTagName('HIGH_PROBA_CLOUDS_PERCENTAGE')[0].firstChild.data)
+        
+        record.update({
+            'product_uri': product_uri,
+            'sensing_start': sensing_start,
+            'granule_path': granule_path,
+            'img_name': img_name,
+            'cloud_cover': cloud_coverage,
+            'cloud_shadow': cloud_shadow,
+            'medium_clouds': medium_clouds,
+            'high_clouds': high_clouds,
+            'geom_wkt': geom_wkt
+        })
+        
+    except Exception as e:
+        print(f'Failed to parse XML: {e}')
+        
+    return record
+
+def convert_footprint_to_wkt(footprint):
+    """Convert footprint string to WKT format"""
+    coords = footprint.strip().split(' ')
+    wkt_coords = []
+    for i in range(0, len(coords), 2):
+        wkt_coords.append(f"{coords[i+1]} {coords[i]}")
+    return f"MULTIPOLYGON ((({','.join(wkt_coords)})))"
+
+def zip2rec(fn_zip):
+    id_img = os.path.splitext(os.path.basename(fn_zip))[0]
+    archive = zipfile.ZipFile(fn_zip, 'r')
+    fn_xml = archive.open(os.path.join(f'{id_img}.SAFE', 'MTD_MSIL2A.xml'))
+    rec = parse_metadata(fn_xml)
+    rec['geometry'] = wkt.loads(rec['geom_wkt'])
+    thumb = archive.open(os.path.join(f'{id_img}.SAFE', f'{id_img}-ql.jpg'))
+    thumb = iio.imread(thumb)
+    rec['thumb'] = thumb
+    return rec
+
+def build_catalog(path, fn='catalog'):
+    '''
+    fn: filename or None
+    '''
+    list_fnames = glob(os.path.join(path, 'S2*.zip'))
+
+    list_rec = []
+    for fn_zip in list_fnames:
+        rec = zip2rec(fn_zip)
+        list_rec.append(rec)
+
+    gdf = gpd.GeoDataFrame(list_rec, crs='EPSG:4326').drop(columns='geom_wkt')
+    if fn is not None:
+        fn_geojson = os.path.join(path, f"{fn}.geojson")
+        gdf.to_file(fn_geojson, driver='GeoJSON')
+        return fn_geojson
+    else:
+        return gdf
+
+def make_full_name(rec, band):
+    dict_bands = {
+        'B2': ['B02', '10m'],
+        'B3': ['B03', '10m'],
+        'B4': ['B04', '10m'],
+        'B8': ['B08', '10m'],
+        'B5': ['B05', '20m'],
+        'B6': ['B06', '20m'],
+        'B7': ['B07', '20m'],
+        'B8A': ['B8A', '20m'],
+        'B11': ['B11', '20m'],
+        'B12': ['B12', '20m'],
+        'SCL': ['SCL', '20m'],
+    }
+    fn_template = os.path.join(
+        '{p0}', 'GRANULE',
+        '{p1}', 'IMG_DATA', "R{p2}",
+        '{p3}_{p4}_{p2}.jp2'
+    )
+    return fn_template.format(**{
+        'p0': rec['product_uri'], 
+        'p0b': rec['product_uri'].split('.')[0], 
+        'p1': rec['granule_path'],
+        'p2': dict_bands[band][1],
+        'p3': rec['img_name'], 
+        'p4': dict_bands[band][0],
+    })
+
+def warp(
+    ds, outputBounds, 
+    outputBoundsSRS='EPSG:4326',
+    xRes=10, yRes=10, targetAlignedPixels=True,
+    **kwargs,
+):
+    options_warp = gdal.WarpOptions(
+        format="MEM",
+        outputBounds=outputBounds, 
+        outputBoundsSRS=outputBoundsSRS,
+        xRes=xRes, yRes=yRes, targetAlignedPixels=targetAlignedPixels,
+        **kwargs,
+    )
+    ds_warp = gdal.Warp('', ds, options=options_warp)
+    return ds_warp
+
+def get_ndarray(
+    ds, outputBounds, 
+    outputBoundsSRS='EPSG:4326',
+    xRes=10, yRes=10, targetAlignedPixels=True,
+    **kwargs,
+):
+    ds_warp = warp(
+        ds, outputBounds, 
+        outputBoundsSRS='EPSG:4326',
+        xRes=10, yRes=10, targetAlignedPixels=True,
+        **kwargs
+    )
+    arr = ds_warp.ReadAsArray()
+    ds_warp = None
+    return arr
+

tools/pretraining_data_builder/rsi_process/utils_wv.py

@@ -0,0 +1,237 @@
+import os
+from osgeo import gdal
+import numpy as np
+from datetime import datetime
+import xml.etree.ElementTree as ET
+
+def parse_metadata(meta_xml_file):
+    """Parse the WorldView metadata XML file
+    
+    Args:
+        meta_xml_file: Metadata XML file path
+        
+    Returns:
+        dict: Dictionary containing key metadata information
+    """
+    record = {}
+    
+    try:
+        tree = ET.parse(meta_xml_file)
+        root = tree.getroot()
+        
+        ns = {'imd': root.tag.split('}')[0].strip('{')}
+        
+        # Get basic information
+        record['satellite_id'] = root.find('.//imd:satelliteID', ns).text
+        record['product_type'] = root.find('.//imd:productType', ns).text
+        
+        # Get acquisition time
+        acq_time = root.find('.//imd:firstLineTime', ns).text
+        record['sensing_start'] = datetime.strptime(acq_time, '%Y-%m-%dT%H:%M:%S.%fZ')
+        
+        # Get solar angle
+        record['sun_azimuth'] = float(root.find('.//imd:meanSunAz', ns).text)
+        record['sun_elevation'] = float(root.find('.//imd:meanSunEl', ns).text)
+        
+        # Get satellite angle
+        record['satellite_azimuth'] = float(root.find('.//imd:meanSatAz', ns).text)
+        record['satellite_elevation'] = float(root.find('.//imd:meanSatEl', ns).text)
+        
+        # Get cloud cover
+        cloud_cover = root.find('.//imd:cloudCover', ns)
+        record['cloud_cover'] = float(cloud_cover.text) if cloud_cover is not None else None
+        
+        # Get image range
+        record['ul_lon'] = float(root.find('.//imd:ULLon', ns).text)
+        record['ul_lat'] = float(root.find('.//imd:ULLat', ns).text)
+        record['ur_lon'] = float(root.find('.//imd:URLon', ns).text)
+        record['ur_lat'] = float(root.find('.//imd:URLat', ns).text)
+        record['ll_lon'] = float(root.find('.//imd:LLLon', ns).text)
+        record['ll_lat'] = float(root.find('.//imd:LLLat', ns).text)
+        record['lr_lon'] = float(root.find('.//imd:LRLon', ns).text)
+        record['lr_lat'] = float(root.find('.//imd:LRLat', ns).text)
+        
+        # Build WKT format geometry information
+        record['geom_wkt'] = create_footprint_wkt(record)
+        
+    except Exception as e:
+        print(f"Error parsing metadata: {str(e)}")
+        return None
+        
+    return record
+
+def create_footprint_wkt(record):
+    """Create a WKT format polygon based on corner coordinates
+    
+    Args:
+        record: Dictionary containing corner coordinates
+        
+    Returns:
+        str: WKT format polygon string
+    """
+    coords = [
+        (record['ul_lon'], record['ul_lat']),
+        (record['ur_lon'], record['ur_lat']),
+        (record['lr_lon'], record['lr_lat']),
+        (record['ll_lon'], record['ll_lat']),
+        (record['ul_lon'], record['ul_lat']) 
+    ]
+    
+    coord_str = ', '.join([f"{lon} {lat}" for lon, lat in coords])
+    return f"POLYGON(({coord_str}))"
+
+def get_band_info(ds):
+    """Get the band information of the image
+    
+    Args:
+        ds: GDAL dataset
+        
+    Returns:
+        list: Band information list
+    """
+    bands = []
+    for i in range(1, ds.RasterCount + 1):
+        band = ds.GetRasterBand(i)
+        band_info = {
+            'band_number': i,
+            'data_type': gdal.GetDataTypeName(band.DataType),
+            'nodata_value': band.GetNoDataValue()
+        }
+        bands.append(band_info)
+    return bands
+
+def read_as_array(ds, window=None):
+    """Read image data as a numpy array
+    
+    Args:
+        ds: GDAL dataset
+        window: Read window, format as (xoff, yoff, xsize, ysize)
+        
+    Returns:
+        numpy.ndarray: Image data array
+    """
+    if window is None:
+        return ds.ReadAsArray()
+    else:
+        xoff, yoff, xsize, ysize = window
+        return ds.ReadAsArray(xoff, yoff, xsize, ysize)
+
+def get_image_info(fn_img):
+    """Get basic information of WorldView image
+    
+    Args:
+        fn_img: Image file path
+        
+    Returns:
+        dict: Image information dictionary
+    """
+    ds = gdal.Open(fn_img)
+    if ds is None:
+        raise Exception(f"Cannot open {fn_img}")
+    
+    info = {
+        'width': ds.RasterXSize,
+        'height': ds.RasterYSize,
+        'bands': ds.RasterCount,
+        'projection': ds.GetProjection(),
+        'geotransform': ds.GetGeoTransform(),
+        'band_info': get_band_info(ds)
+    }
+    
+    xml_file = fn_img.replace('.tif', '.xml')
+    if os.path.exists(xml_file):
+        metadata = parse_metadata(xml_file)
+        if metadata:
+            info.update(metadata)
+    
+    ds = None
+    return info
+
+def calculate_stats(fn_img, percentiles=[2, 98]):
+    """Calculate the statistics of the image
+    
+    Args:
+        fn_img: Image file path
+        percentiles: List of percentiles
+        
+    Returns:
+        dict: Statistics dictionary
+    """
+    ds = gdal.Open(fn_img)
+    stats = {}
+    
+    for i in range(1, ds.RasterCount + 1):
+        band = ds.GetRasterBand(i)
+        array = band.ReadAsArray()
+        valid_data = array[array != band.GetNoDataValue()]
+        
+        stats[f'band_{i}'] = {
+            'min': np.min(valid_data),
+            'max': np.max(valid_data),
+            'mean': np.mean(valid_data),
+            'std': np.std(valid_data),
+            'percentiles': {
+                p: np.percentile(valid_data, p)
+                for p in percentiles
+            }
+        }
+    
+    ds = None
+    return stats
+
+def create_quicklook(fn_img, output_file, size=(1024, 1024)):
+    """Create a thumbnail
+    
+    Args:
+        fn_img: Image file path
+        output_file: Output file path
+        size: Output image size
+    """
+    ds = gdal.Open(fn_img)
+    
+    if ds.RasterCount >= 3:
+        r = ds.GetRasterBand(1).ReadAsArray()
+        g = ds.GetRasterBand(2).ReadAsArray()
+        b = ds.GetRasterBand(3).ReadAsArray()
+        
+        def stretch(arr):
+            p2, p98 = np.percentile(arr[arr > 0], (2, 98))
+            return np.clip((arr - p2) / (p98 - p2) * 255, 0, 255).astype(np.uint8)
+        
+        rgb = np.dstack([stretch(r), stretch(g), stretch(b)])
+        
+        from PIL import Image
+        img = Image.fromarray(rgb)
+        img.thumbnail(size)
+        img.save(output_file)
+    
+    ds = None
+
+def warp(ds, outputBounds, 
+         outputBoundsSRS='EPSG:4326',
+         xRes=2, yRes=2, 
+         targetAlignedPixels=True,
+         **kwargs):
+    """Reprojection and resampling
+    
+    Args:
+        ds: GDAL dataset
+        outputBounds: Output range
+        outputBoundsSRS: Output coordinate system
+        xRes, yRes: Output resolution
+        targetAlignedPixels: Whether to align pixels
+        **kwargs: Other GDAL.Warp parameters
+    
+    Returns:
+        GDAL dataset
+    """
+    options_warp = gdal.WarpOptions(
+        format="MEM",
+        outputBounds=outputBounds, 
+        outputBoundsSRS=outputBoundsSRS,
+        xRes=xRes, yRes=yRes, 
+        targetAlignedPixels=targetAlignedPixels,
+        **kwargs
+    )
+    ds_warp = gdal.Warp('', ds, options=options_warp)
+    return ds_warp 

tools/pretraining_data_builder/run_data_builder.sh

@@ -0,0 +1,11 @@
+#! /bin/bash
+source activate data_builder
+export USERNAME=your_username
+export PASSWORD=your_password
+export API_KEY=your_api_key
+
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+LMDB_PATH=your_lmdb_path
+
+python rsi_pipeline/data_builder.py $LMDB_PATH

tools/run.py

@@ -0,0 +1,44 @@
+from antmmf.utils.env import setup_compatibility
+from antmmf.utils.flags import flags
+from antmmf.run import plain_run
+
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(__file__))
+from lib import *  # noqa  make sure all modules have been registered.
+
+usage = """
+    Usage:
+        python tools/run.py --config configs/foo/bar.yml [OPTIONS] [OPTS]
+
+    Options:
+        --config_override override.yml   configurations from this file will override the --config one. like
+            python tools/run.py --config configs/foo/bar.yml --config_override my_foobar.yml
+
+        --local_rank local rank of your machine, used in parallel mode
+
+        OPTS: override specific value in config, like
+            python tools/run.py --config configs/foo/bar.yml \\
+                training_parameters.device cuda:0 \\
+                training_parameters.max_epochs 5 \\
+                task_attributes.hateful_memes.dataset_attributes.foo.images.train \\
+                "[foo/defaults/images]"
+
+    Priority:
+        OPTS OVERRIDE --config_override OVERRIDE --config, see antmmf/common/build.py::build_config for details
+"""
+
+
+def run():
+    parser = flags.get_parser()
+    try:
+        args = parser.parse_args()
+        plain_run(args)
+    except SystemExit:
+        exit(2)
+
+
+if __name__ == "__main__":
+    setup_compatibility()
+    run()

tools/run_1shot.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+set -x
+export PYTHONPATH=`pwd`:$PYTHONPATH
+cd antmmf
+export PYTHONPATH=`pwd`:$PYTHONPATH
+cd ../
+
+export CUDA_VISIBLE_DEVICES=$1
+dataset_name=$2
+
+CONFIG_PATH=configs/eval_skysense_pp_${dataset_name}.yml
+MODEL_PATH=pretrain/skysensepp_release.ckpt
+
+SAVE_DIR=eval/${dataset_name}_1shot/save
+GT_DIR=eval_datasets/${dataset_name}/targets
+GT_LIST_PATH=eval_datasets/${dataset_name}/val.txt
+
+mkdir -p $SAVE_DIR
+
+# predictor
+python lib/predictors/${dataset_name}_1shot.py \
+    --model_path $MODEL_PATH \
+    --config $CONFIG_PATH \
+    --save_dir $SAVE_DIR \
+    --seed 0
+
+# eval
+python lib/evaluation/segm_eval_base.py \
+   --pred_dir ${SAVE_DIR} \
+   --gt_dir ${GT_DIR} \
+   --gt_list_path ${GT_LIST_PATH} \
+   --gt_suffix '.png' \
+   --dataset_name ${dataset_name} \
+   --dist_type 'abs' \
+   --model_name skysense++_1shot

tools/run_finetune.sh

@@ -0,0 +1,9 @@
+#!/bin/bash
+
+cd finetune/
+cfg=$1
+gpu_num=8
+
+bash finetune/tools/dist_train.sh $cfg $gpu_num
+
+

tools/run_pretrain.sh

@@ -0,0 +1,24 @@
+#!/bin/sh
+set -x
+export PYTHONPATH=`pwd`:$PYTHONPATH
+cd antmmf
+export PYTHONPATH=`pwd`:$PYTHONPATH
+cd ..
+export NCCL_DEBUG=INFO
+
+export OMP_NUM_THREADS=4
+
+NUM_GPU=8
+
+CONFIG_FILE=configs/pretrain_skysensepp.yml
+SAVE_DIR=save/skysensepp_pretrain
+
+mkdir -p ${SAVE_DIR}/$1/
+
+pip install lmdb
+nohup python -m antmmf.utils.launch --nproc_per_node=${NUM_GPU} --master_port 12345 --nnodes=4 --node_rank=$1 --master_addr=$2 tools/run.py --config $CONFIG_FILE \
+    training_parameters.distributed True \
+    training_parameters.save_dir ${SAVE_DIR} > ${SAVE_DIR}/$1/nohup.log 2>&1 &
+
+sleep 3s
+tail -f ${SAVE_DIR}/$1/nohup.log