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Remote Sensing

2026

  • NISAR Soil Moisture Analytics: Python tools for analyzing NISAR satellite data for soil moisture retrieval and related surface parameter estimation. [Keywords: NISAR SAR soil moisture remote sensing Python]

2025

  • Potato: Pan Sharpening Neural Net: A compact neural network-based pansharpener for satellite imagery focused on accurate perceptual color output for human viewers. [Keywords: pan sharpening deep learning satellite imagery neural network Python]

  • OmniCloudMask: A Python library for fast, accurate cloud and cloud shadow segmentation in satellite imagery, supporting Sentinel-2, Landsat 8, and PlanetScope at 10-50m resolution. [Keywords: cloud masking Sentinel-2 Landsat deep learning Python remote sensing]

  • Methane mapping of landfills: Nature paper on mapping methane emissions from landfills using Landsat downscaling and satellite remote sensing data. [Keywords: methane Landsat remote sensing atmospheric climate]

  • Sentinel-2 Super Resolution (Colab): Google Colab notebook for automated Sentinel-2 super-resolution inference using deep learning. [Keywords: Sentinel-2 super resolution deep learning Python remote sensing]

  • Playing with Fire: Satellite images and Cellular Automata for Wildfire Spread: Using satellite imagery combined with cellular automata models to predict and visualize wildfire spread patterns. [Keywords: wildfire cellular automata satellite imagery remote sensing prediction]

  • Geospatial Foundational Disappointments: Article critiquing geospatial foundation model performance, arguing undertraining and lack of clear objectives are primary causes. [Keywords: foundation models geospatial AI remote sensing machine learning]

2024

  • Sentinel-2 Super Resolution: Tools to enhance Sentinel-2 satellite imagery by upscaling 10-meter resolution bands to 5-meter resolution using deep learning models trained on the Sen2Venµs dataset. [Keywords: Sentinel-2 super resolution deep learning EVOLAND Horizon Europe]

  • EUDR Compliance Solutions: Satelligence and Nadar.earth provide EUDR (EU Deforestation Regulation) compliance monitoring using satellite remote sensing. [Keywords: EUDR deforestation compliance Sentinel satellite monitoring]

  • Agroforestry Methods PDF: World Agroforestry Center publication on agroforestry mapping methods and techniques. [Keywords: agroforestry mapping remote sensing]

  • Automated road detection in Congo basin: Paper on automated forest road mapping and detection in the Congo basin using satellite remote sensing. [Keywords: road detection Congo basin forest mapping remote sensing deep learning]

  • Microsoft Planetary Computer STAC API Example: Tutorial on using the Microsoft Planetary Computer STAC API with platform filters for Landsat 8/9 data access. [Keywords: Planetary Computer STAC Landsat Python cloud-native]

  • Ian Woodhouse on Test/Train split for Regression: Twitter thread on best practices for test/train splitting in remote sensing regression workflows. [Keywords: machine learning remote sensing regression train/test split]

Earlier

  • satellite-image-deep-learning: Comprehensive resource list for performing deep learning on satellite imagery. [Keywords: deep learning satellite imagery resources remote sensing]

  • SAR Polarimetry Open Access Book: Open access Springer book on SAR polarimetry theory and applications. [Keywords: SAR polarimetry radar remote sensing]

  • SatClip location embedding: Location embedding approach extracting features that describe geographic location from Sentinel-2 imagery, similar to Planet georeferencing data. [Keywords: location embedding Sentinel-2 geospatial AI foundation models]

  • Sea Surface Salinity - Ocean vs Freshwater Delineation: ISPRS paper on using remote sensing for sea surface salinity mapping and ocean-freshwater boundary delineation. [Keywords: sea surface salinity ocean remote sensing satellite]

  • SIAM Land Cover: NOAA seminar on SIAM (Satellite Image Automatic Mapper) for automatic land cover classification. [Keywords: land cover classification NOAA satellite automatic mapping]