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geomaster Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
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16 fichiers name geomaster description Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task. license MIT License metadata {"version":"1.0","skill-author":"K-Dense Inc."}
GeoMaster
Comprehensive geospatial science skill covering GIS, remote sensing, spatial analysis, and ML for Earth observation across 70+ topics with 500+ code examples in 8 programming languages.
Installation
conda install -c conda-forge gdal rasterio fiona shapely pyproj geopandas
conda install -c conda-forge rsgislib
uv pip install torchgeo earthengine-api
uv pip install scikit-learn xgboost torch-geometric
uv pip install osmnx networkx folium keplergl
uv pip install cartopy contextily mapclassify
Quick Start
NDVI from Sentinel-2 import rasterio
import numpy as np
with rasterio.open ('sentinel2.tif' ) as src:
red = src.read(4 ).astype(float )
nir = src.read(8 ).astype(float )
ndvi = (nir - red) / (nir + red + 1e-8 )
ndvi = np.nan_to_num(ndvi, nan=0 )
profile = src.profile
profile.update(count=1 , dtype=rasterio.float32)
with rasterio.open ('ndvi.tif' , 'w' , **profile) as dst:
dst.write(ndvi.astype(rasterio.float32), 1 )
Spatial Analysis with GeoPandas import geopandas as gpd
zones = gpd.read_file('zones.geojson' )
points = gpd.read_file('points.geojson' )
if zones.crs != points.crs:
points = points.to_crs(zones.crs)
joined = gpd.sjoin(points, zones, how='inner' , predicate='within' )
stats = joined.groupby('zone_id' ).agg({
'value' : ['count' , 'mean' , 'std' , 'min' , 'max' ]
}).round (2 )
Google Earth Engine Time Series import ee
import pandas as pd
ee.Initialize(project='your-project' )
roi = ee.Geometry.Point([-122.4 , 37.7 ]).buffer(10000 )
s2 = (ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED' )
.filterBounds(roi)
.filterDate('2020-01-01' , '2023-12-31' )
.filter (ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE' , 20 )))
def add_ndvi (img ):
return img.addBands(img.normalizedDifference(['B8' , 'B4' ]).rename('NDVI' ))
s2_ndvi = s2.map (add_ndvi)
def extract_series (image ):
stats = image.reduceRegion(ee.Reducer.mean(), roi.centroid(), scale=10 , maxPixels=1e9 )
return ee.Feature(None , {'date' : image.date().format ('YYYY-MM-dd' ), 'ndvi' : stats.get('NDVI' )})
series = s2_ndvi.map (extract_series).getInfo()
df = pd.DataFrame([f['properties' ] for f in series['features' ]])
df['date' ] = pd.to_datetime(df['date' ])
Core Concepts
Data Types Type Examples Libraries Vector Shapefile, GeoJSON, GeoPackage GeoPandas, Fiona, GDAL Raster GeoTIFF, NetCDF, COG Rasterio, Xarray, GDAL Point Cloud LAS, LAZ Laspy, PDAL, Open3D
Coordinate Systems
EPSG:4326 (WGS 84) - Geographic, lat/lon, use for storageEPSG:3857 (Web Mercator) - Web maps only (don't use for area/distance!)EPSG:326xx/327xx (UTM) - Metric calculations, <1% distortion per zoneUse gdf.estimate_utm_crs() for automatic UTM detection
assert gdf1.crs == gdf2.crs, "CRS mismatch!"
gdf_metric = gdf.to_crs(gdf.estimate_utm_crs())
area_sqm = gdf_metric.geometry.area
OGC Standards
WMS : Web Map Service - raster mapsWFS : Web Feature Service - vector dataWCS : Web Coverage Service - raster coverageSTAC : Spatiotemporal Asset Catalog - modern metadata
Common Operations
Spectral Indices def calculate_indices (image_path ):
"""NDVI, EVI, SAVI, NDWI from Sentinel-2."""
with rasterio.open (image_path) as src:
B02, B03, B04, B08, B11 = [src.read(i).astype(float ) for i in [1 ,2 ,3 ,4 ,5 ]]
ndvi = (B08 - B04) / (B08 + B04 + 1e-8 )
evi = 2.5 * (B08 - B04) / (B08 + 6 *B04 - 7.5 *B02 + 1 )
savi = ((B08 - B04) / (B08 + B04 + 0.5 )) * 1.5
ndwi = (B03 - B08) / (B03 + B08 + 1e-8 )
return {'NDVI' : ndvi, 'EVI' : evi, 'SAVI' : savi, 'NDWI' : ndwi}
Vector Operations
gdf_proj = gdf.to_crs(gdf.estimate_utm_crs())
gdf['buffer_1km' ] = gdf_proj.geometry.buffer(1000 )
intersects = gdf[gdf.geometry.intersects(other_geometry)]
contains = gdf[gdf.geometry.contains(point_geometry)]
gdf['centroid' ] = gdf.geometry.centroid
gdf['simplified' ] = gdf.geometry.simplify(tolerance=0.001 )
intersection = gpd.overlay(gdf1, gdf2, how='intersection' )
union = gpd.overlay(gdf1, gdf2, how='union' )
Terrain Analysis def terrain_metrics (dem_path ):
"""Calculate slope, aspect, hillshade from DEM."""
with rasterio.open (dem_path) as src:
dem = src.read(1 )
dy, dx = np.gradient(dem)
slope = np.arctan(np.sqrt(dx**2 + dy**2 )) * 180 / np.pi
aspect = (90 - np.arctan2(-dy, dx) * 180 / np.pi) % 360
az_rad, alt_rad = np.radians(315 ), np.radians(45 )
hillshade = (np.sin(alt_rad) * np.sin(np.radians(slope)) +
np.cos(alt_rad) * np.cos(np.radians(slope)) *
np.cos(np.radians(aspect) - az_rad))
return slope, aspect, hillshade
Network Analysis import osmnx as ox
import networkx as nx
G = ox.graph_from_place('San Francisco, CA' , network_type='drive' )
G = ox.add_edge_speeds(G).add_edge_travel_times(G)
orig = ox.distance.nearest_nodes(G, -122.4 , 37.7 )
dest = ox.distance.nearest_nodes(G, -122.3 , 37.8 )
route = nx.shortest_path(G, orig, dest, weight='travel_time' )
Image Classification from sklearn.ensemble import RandomForestClassifier
import rasterio
from rasterio.features import rasterize
def classify_imagery (raster_path, training_gdf, output_path ):
"""Train RF and classify imagery."""
with rasterio.open (raster_path) as src:
image = src.read()
profile = src.profile
transform = src.transform
X_train, y_train = [], []
for _, row in training_gdf.iterrows():
mask = rasterize([(row.geometry, 1 )],
out_shape=(profile['height' ], profile['width' ]),
transform=transform, fill=0 , dtype=np.uint8)
pixels = image[:, mask > 0 ].T
X_train.extend(pixels)
y_train.extend([row['class_id' ]] * len (pixels))
rf = RandomForestClassifier(n_estimators=100 , max_depth=20 , n_jobs=-1 )
rf.fit(X_train, y_train)
prediction = rf.predict(image.reshape(image.shape[0 ], -1 ).T)
prediction = prediction.reshape(profile['height' ], profile['width' ])
profile.update(dtype=rasterio.uint8, count=1 )
with rasterio.open (output_path, 'w' , **profile) as dst:
dst.write(prediction.astype(rasterio.uint8), 1 )
return rf
Modern Cloud-Native Workflows
STAC + Planetary Computer import pystac_client
import planetary_computer
import odc.stac
catalog = pystac_client.Client.open (
"https://planetarycomputer.microsoft.com/api/stac/v1" ,
modifier=planetary_computer.sign_inplace,
)
search = catalog.search(
collections=["sentinel-2-l2a" ],
bbox=[-122.5 , 37.7 , -122.3 , 37.9 ],
datetime="2023-01-01/2023-12-31" ,
query={"eo:cloud_cover" : {"lt" : 20 }},
)
data = odc.stac.load(
list (search.get_items())[:5 ],
bands=["B02" , "B03" , "B04" , "B08" ],
crs="EPSG:32610" ,
resolution=10 ,
)
ndvi = (data.B08 - data.B04) / (data.B08 + data.B04)
Cloud-Optimized GeoTIFF (COG) import rasterio
from rasterio.session import AWSSession
session = AWSSession(aws_access_key_id=..., aws_secret_access_key=...)
with rasterio.open ('s3://bucket/path.tif' , session=session) as src:
window = ((1000 , 2000 ), (1000 , 2000 ))
subset = src.read(1 , window=window)
with rasterio.open ('output.tif' , 'w' , **profile,
tiled=True , blockxsize=256 , blockysize=256 ,
compress='DEFLATE' , predictor=2 ) as dst:
dst.write(data)
from rio_cogeo.cogeo import cog_validate
cog_validate('output.tif' )
Performance Tips
gdf.sindex
with rasterio.open ('large.tif' ) as src:
for i, window in src.block_windows(1 ):
block = src.read(1 , window=window)
import dask.array as da
dask_array = da.from_rasterio('large.tif' , chunks=(1 , 1024 , 1024 ))
gdf.to_file('output.gpkg' , use_arrow=True )
from osgeo import gdal
gdal.SetCacheMax(2 **30 )
rf = RandomForestClassifier(n_jobs=-1 )
Best Practices
Always check CRS before spatial operationsUse projected CRS for area/distance calculationsValidate geometries : gdf = gdf[gdf.is_valid]Handle missing data : gdf['geometry'] = gdf['geometry'].fillna(None)Use efficient formats : GeoPackage > Shapefile, Parquet for large dataApply cloud masking to optical imageryPreserve lineage for reproducible researchUse appropriate resolution for your analysis scale
Detailed Documentation
Coordinate Systems Core Libraries Remote Sensing Machine Learning GIS Software Scientific Domains Advanced GIS Big Data Industry Applications Programming Languages Data Sources Troubleshooting Code Examples
GeoMaster covers everything from basic GIS operations to advanced remote sensing and machine learning.
