| satellite-image-deep-learning/techniques |
10,034 |
|
0 |
0 |
about 1 month ago |
0 |
|
2 |
apache-2.0 |
|
| Techniques for deep learning with satellite & aerial imagery |
| chrieke/awesome-satellite-imagery-datasets |
3,381 |
|
0 |
0 |
over 3 years ago |
0 |
|
0 |
mit |
|
| 🛰️ List of satellite image training datasets with annotations for computer vision and deep learning |
| 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 |
| 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. |
| ternaus/TernausNetV2 |
500 |
|
0 |
0 |
almost 6 years ago |
0 |
|
14 |
bsd-3-clause |
Jupyter Notebook |
| TernausNetV2: Fully Convolutional Network for Instance Segmentation |
| neptune-ai/open-solution-mapping-challenge |
363 |
|
0 |
0 |
about 5 years ago |
0 |
|
53 |
mit |
Python |
| Open solution to the Mapping Challenge :earth_americas: |
| developmentseed/label-maker |
358 |
|
0 |
0 |
over 5 years ago |
18 |
November 19, 2020 |
40 |
mit |
Python |
| Data Preparation for Satellite Machine Learning |
| wgcban/ChangeFormer |
335 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
mit |
Python |
| [IGARSS'22]: A Transformer-Based Siamese Network for Change Detection |
| chrieke/InstanceSegmentation_Sentinel2 |
300 |
|
0 |
0 |
about 3 years ago |
0 |
|
1 |
|
Jupyter Notebook |
| 🌱 Deep Learning for Instance Segmentation of Agricultural Fields - Master thesis |
| sshuair/torchsat |
285 |
|
0 |
0 |
over 5 years ago |
4 |
April 20, 2018 |
3 |
mit |
Python |
| 🔥TorchSat 🌏 is an open-source deep learning framework for satellite imagery analysis based on PyTorch. |
