Geospatial & Remote Sensing Notes

These indices and articles compile core reference logs on coordinate reference systems, raster IO structures, state estimation algorithms (Kalman Filters) used in localization, and foundational hyperspectral physics.

📖 Key Reference Journals & Guides

• Bidhya's Geospatial Python Stack: Standard import guide and optimized reference codes for GeoPandas, Rasterio, Rioxarray, and Xarray.
• Kalman Filter & LiDAR Fusion: State estimation mathematics, predict/update loop configurations, and vehicle localization sensor alignments.
• Hyperspectral Remote Sensing Theory: Differences between continuous narrow-band hyperspectral arrays (e.g., AVIRIS, PACE) vs discrete broad-band multispectral sensors.

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Methodological Priorities

In geospatial workflows, my emphasis is on physical accuracy mixed with numerical efficiency: * Grid Consistency: Handling mismatched spatial resolutions and projections using highly-parallelised rioxarray or gdal abstractions. * Dynamic Range Correctness: Empirical satellite bias corrections using satellite altimetry footprints (e.g., ICESat-2) to validate Digital Elevation Models.