Remote Sensing

Summary: Notes on satellite and aerial imagery for Earth observation. Last updated: 2026-08-20

---

2026

• Montado Tree Mapping: Automated individual tree detection and vegetation classification from LiDAR, with examples of UMAP unsupervised classification. Related: Machine_Learning. Keywords: LiDAR, tree detection, vegetation classification, UMAP, unsupervised classification, remote sensing

• APEx Algorithm Catalogue: ESA's platform for discovering and accessing Earth observation algorithms and services, letting EO projects showcase their algorithms and results to a broader community. Related: STAC. Keywords: ESA, algorithm catalogue, Earth observation, remote sensing, platform

• Agribound: A Python toolkit for agricultural field boundary delineation from satellite imagery, combining seven approaches including object detection, semantic segmentation, vision transformers, foundation models, and multi-engine ensembling, with support for Landsat, Sentinel-2, HLS, NAIP, SPOT, and pre-computed embeddings. Related: Agriculture, Python, Deep_Learning. Keywords: agriculture, field boundaries, satellite imagery, Python, deep learning, remote sensing, Sentinel-2, Landsat

• NISAR Soil Moisture Analytics: Python tools for analyzing NISAR satellite data for soil moisture retrieval and related surface parameter estimation. Related: Agriculture, Python, Data. 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. Related: Deep_Learning, Python. 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. Related: Deep_Learning, Python. 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. Related: Climate, 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. Related: Deep_Learning, Python. 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. Related: Climate. 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. Related: Deep_Learning, Machine_Learning. 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. Related: Deep_Learning, Tools. 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. Related: Agriculture. Keywords: EUDR, deforestation, compliance, Sentinel, satellite monitoring

• Agroforestry Methods PDF: World Agroforestry Center publication on agroforestry mapping methods and techniques. Related: Agriculture, Cartography. 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. Related: Deep_Learning. 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. Related: STAC, Python, CNG. 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. Related: Machine_Learning, Deep_Learning. Keywords: machine learning, remote sensing, regression, train/test split

Earlier

• satellite-image-deep-learning: Comprehensive resource list for performing deep learning on satellite imagery. Related: Deep_Learning. Keywords: deep learning, satellite imagery, resources, remote sensing

• SAR Polarimetry Open Access Book: Open access Springer book on SAR polarimetry theory and applications. Related: Papers. 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. Related: Deep_Learning, 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. Related: Water_Resources. Keywords: sea surface salinity, ocean, remote sensing, satellite

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