Lists (6)
Stars
This repository contains the official implementation of the paper "LandSegmenter: Towards a Flexible Foundation Model for Land Use and Land Cover Mapping".
2nd ESA NASA workshop hands on session contents
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, PyTorch, and Hugging Face libraries.
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
Claude Code configurations for R development.
66 Specialized Skills for Full-Stack Developers. Transform Claude Code into your expert pair programmer.
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chem…
GvdDool / tessera-CVPR2026-aplhaV1
Forked from ucam-eo/tessera[CVPR26] TESSERA is a foundation model that can process time-series satellite imagery for applications such as land classification and canopy height prediction. Developed at the University of Cambr…
A curated list of awesome tools, tutorials, code, projects, links, stuff about Earth Observation, Geospatial Satellite Imagery
[IEEE TGRS 2025] Be the Change You Want to See: Revisiting Remote Sensing Change Detection Practices
UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations
Distributed storage, processing and access for satellite imagery and Earth observation products
Defines a STAC extension for describing collections of geospatial vector embeddings.
🛁 Clean Code concepts adapted for Python
Clean Code concepts adapted for TypeScript
[CVPR26] TESSERA is a foundation model that can process time-series satellite imagery for applications such as land classification and canopy height prediction. Developed at the University of Cambr…
GeoAI: Artificial Intelligence for Geospatial Data
TerraFM is a scalable foundation model for unified multisensor Earth observation, trained on 18.7M Sentinel-1/2 tiles and achieving state-of-the-art results on GEO-Bench and Copernicus-Bench.
Earth system foundation model data, training, and eval
A curated list of papers that focus on how to represent Earth data in embedding space — spatial, temporal, or semantic — and how those embeddings behave or are applied.
LLM Council works together to answer your hardest questions
TerraMind is the first any-to-any generative foundation model for Earth Observation, built by IBM and ESA.
Implementation of Convolutional LSTM in PyTorch.
PDFM Embeddings: location-based vectors for geo-spatial analysis.
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.