aleguillou1

MASA-SegNet

remote_sensing_segmentation

Semantic segmentation for remote sensing images

U-Net-Semantic-Segmentation-Aerial-Images

GeoSeg

UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery, ISPRS. Also, including other vision transformers and CNNs for satellite, aerial image and UAV image segmentation.

UNet_keras_for_RSimage

This is a code of U-Net Semantic Segmentation for Remote Sensing Images,mainly for learning and communication

awesome-satellite-imagery-datasets

🛰️ List of satellite image training datasets with annotations for computer vision and deep learning

segment-geospatial

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

segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

extract_water

Extract water from nIR imagery

awesome-earthobservation-code

A curated list of awesome tools, tutorials, code, projects, links, stuff about Earth Observation, Geospatial Satellite Imagery

mmsegmentation

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

techniques

Techniques for deep learning with satellite & aerial imagery

remote-sensing-deep-learning

EMRT

A Semantic Segmentation Method for Remote Sensing Images Combining CNN and Transformer.

CLCFormer

IEEE GRSL: Integrating spatial details with long-range contexts for semantic segmentation of very high resolution remote sensing images

awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

dl-semantic-segmentation

Deep-learning applied to semantic segmentation of remote sensing data

SemanticSegmentation_DL

Resources of semantic segmantation based on Deep Learning model

FLAIR-2

Semantic segmentation from multi-source optical data (baseline for the FLAIR#2 challenge)

Label-Pixels

Label-Pixels is the tool for semantic segmentation of remote sensing images using Fully Convolutional Networks. Initially, it is designed for extracting the road network from remote sensing imagery and now, it can be used to extract different features from remote sensing imagery.

otbtf

Deep learning with otb (mirror of https://forgemia.inra.fr/remi.cresson/otbtf)

localGPT

Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.

Python-Hydrology-Tools

:droplet: This repository holds a list of open source Python packages interesting to Hydrologists