| chrieke/awesome-satellite-imagery-datasets |
3,381 |
|
0 |
0 |
almost 4 years ago |
0 |
|
0 |
mit |
|
| 🛰️ List of satellite image training datasets with annotations for computer vision and deep learning |
| obss/sahi |
3,340 |
|
0 |
12 |
about 2 years ago |
101 |
November 05, 2023 |
11 |
mit |
Python |
| Framework agnostic sliced/tiled inference + interactive ui + error analysis plots |
| microsoft/torchgeo |
2,067 |
|
0 |
4 |
about 2 years ago |
10 |
November 10, 2023 |
123 |
mit |
Python |
| TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data |
| azavea/raster-vision |
1,956 |
|
0 |
7 |
about 2 years ago |
11 |
October 17, 2023 |
42 |
other |
Python |
| An open source library and framework for deep learning on satellite and aerial imagery. |
| jasonmanesis/Satellite-Imagery-Datasets-Containing-Ships |
561 |
|
0 |
0 |
about 1 year ago |
0 |
|
0 |
mit |
|
| This repository provides a comprehensive list of radar and optical satellite datasets curated for ship detection, classification, semantic segmentation, and instance segmentation tasks. These datasets are ideal for applications in computer vision, machine learning, remote sensing, and maritime analysis. |
| developmentseed/label-maker |
358 |
|
0 |
0 |
over 5 years ago |
18 |
November 19, 2020 |
40 |
mit |
Python |
| Data Preparation for Satellite Machine Learning |
| kscottz/PythonFromSpace |
298 |
|
0 |
0 |
about 8 years ago |
0 |
|
6 |
bsd-3-clause |
Jupyter Notebook |
| Python Examples for Remote Sensing |
| manideep2510/eye-in-the-sky |
248 |
|
0 |
0 |
over 3 years ago |
0 |
|
6 |
apache-2.0 |
Python |
| Satellite Image Classification using semantic segmentation methods in deep learning |
| deepVector/geospatial-machine-learning |
196 |
|
0 |
0 |
almost 8 years ago |
0 |
|
0 |
mit |
|
| A curated list of resources focused on Machine Learning in Geospatial Data Science. |
| Z-Zheng/FarSeg |
114 |
|
0 |
0 |
over 2 years ago |
0 |
|
1 |
apache-2.0 |
Python |
| Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery (CVPR 2020) https://arxiv.org/pdf/2011.09766.pdf |
