Tools tagged with "Deep Learning"

A unified Python toolkit for obtaining embeddings from any Remote Sensing Foundation Model with a single line of code.

This book introduces `sits`, an open-source R package for analyzing big Earth observation data using satellite image time series and data cubes.

Master remote sensing image acquisition, analysis, and applications, from fundamentals to deep learning techniques.

A Python library for high-accuracy water segmentation in satellite and aerial imagery using deep learning and spectral indices.

An open-source deep-learning framework for exploring, building, and deploying AI weather and climate workflows.

A foundation model for processing time-series satellite imagery to extract temporal patterns for Earth observation.

This Python library converts diverse geospatial datasets into graph structures for advanced GNN and spatial analysis.

A unified framework for end-to-end segmentation of forest LiDAR 3D point clouds.

This course provides essential Machine Learning skills tailored for Earth Observation research.

A universal feed-forward model for metric 3D reconstruction across diverse inputs and tasks.

A global, temporal dataset tracking solar and wind energy installations using satellite imagery and deep learning.

A powerful Python package for integrating artificial intelligence with geospatial data analysis and visualization.

A standard for curating, sharing, and combining large-scale Earth Observation datasets for AI and ML.

An acceleration framework for 3D Gaussian Splatting, significantly reducing training times while maintaining high rendering quality.

A Substack publication delivering news, technical posts, and research focused on Earth Observation.

A Python toolkit designed for fine-tuning Geospatial Foundation Models (GFMs) for various applications.

A curated, long list of geospatial tools and resources on GitHub.

A Modern Library for 3D Data Processing

Generate and process large-scale geospatial datasets with Google Earth Engine for AI research.

AI-powered geo-location analysis using Google's Gemini API to uncover photo locations.

A comprehensive dataset of annual satellite embeddings, provided as Cloud-Optimized GeoTIFF (COG) files.

Maps Are Everywhere. These are conversations with those building them.

Collating all open-source datasets, software tools and deployment platforms related to open-source agriculture.

Fusing irregular and asynchronous SAR-optical time series to reconstruct reflectance at any given time.

CDrone provides a synthetic drone dataset and data augmentation for monocular 3D object detection.

A collaborative deep learning framework for conservation, enabling the creation and sharing of AI models.

Provides spaceborn precipitation estimates for Africa using deep learning and geostationary imagery.

Current, historical, and projected climate data for North America, South America, Europe, and Africa.

A novel, deep-learning-based climate record of ice-particle concentrations in the atmosphere.

A curated list of conservation-related labeled datasets not hosted on LILA.

A PyTorch implementation of Google Research's MetNet and MetNet-2 for advanced weather forecasting.

Providing diverse datasets for research in computer vision, image processing, and pattern recognition.

A curated collection of papers, data, and repositories related to super-resolution.

A state-of-the-art deep learning library for parsing multinational street addresses.

AI models for classifying wildlife in images from motion-triggered camera traps.

A Python package for segmenting geospatial data with the Segment Anything Model (SAM).