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Prepare datasets

It is recommended to symlink the dataset root to $SegEarth-OV/data. If your folder structure is different, you may need to change the corresponding paths in config files.

SegEarth-OV
├── data
│   ├── OpenEarthMap 
│   │   ├── img_dir
│   │   │   ├── val
│   │   ├── ann_dir
│   │   │   ├── val
│   ├── loveDA
│   │   ├── img_dir
│   │   │   ├── train
│   │   │   ├── val
│   │   │   ├── test
│   │   ├── ann_dir
│   │   │   ├── train
│   │   │   ├── val
│   ├── potsdam
│   │   ├── img_dir
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── ann_dir
│   │   │   ├── train
│   │   │   ├── val
│   ├── vaihingen
│   │   ├── img_dir
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── ann_dir
│   │   │   ├── train
│   │   │   ├── val
│   ├── iSAID
│   │   ├── img_dir
│   │   │   ├── train
│   │   │   ├── val
│   │   │   ├── test
│   │   ├── ann_dir
│   │   │   ├── train
│   │   │   ├── val
│   ├── UAVid 
│   │   ├── img_dir
│   │   │   ├── test
│   │   ├── ann_dir
│   │   │   ├── test
│   ├── UDD5 
│   │   ├── train
│   │   │   ├── src
│   │   │   ├── gt
│   │   ├── val
│   │   │   ├── src
│   │   │   ├── gt
│   ├── VDD 
│   │   ├── train
│   │   │   ├── src
│   │   │   ├── gt
│   │   ├── val
│   │   │   ├── src
│   │   │   ├── gt
│   │   ├── test
│   │   │   ├── src
│   │   │   ├── gt

Semantic Segmentation

OpenEarthMap

The data could be downloaded from here.

For OpenEarthMap dataset, please run the following command to re-organize the dataset.

python tools/dataset_converters/openearthmap.py /path/to/OpenEarthMap

We only use OpenEarthMap_wo_xBD.

LoveDA

The data could be downloaded from Google Drive here.

Or it can be downloaded from zenodo, you should run the following command:

# Download Train.zip
wget https://zenodo.org/record/5706578/files/Train.zip
# Download Val.zip
wget https://zenodo.org/record/5706578/files/Val.zip
# Download Test.zip
wget https://zenodo.org/record/5706578/files/Test.zip

For LoveDA dataset, please run the following command to re-organize the dataset.

python tools/dataset_converters/loveda.py /path/to/loveDA

Using trained model to predict test set of LoveDA and submit it to server can be found here.

More details about LoveDA can be found here.

ISPRS Potsdam

The Potsdam dataset is for urban semantic segmentation used in the 2D Semantic Labeling Contest - Potsdam.

The dataset can be requested at the challenge homepage. Or download on BaiduNetdisk，password：mseg, Google Drive and OpenDataLab. The '2_Ortho_RGB.zip' and '5_Labels_all_noBoundary.zip' are required.

For Potsdam dataset, please run the following command to re-organize the dataset.

python tools/dataset_converters/potsdam.py /path/to/potsdam

In our default setting, it will generate 3456 images for training and 2016 images for validation.

ISPRS Vaihingen

The Vaihingen dataset is for urban semantic segmentation used in the 2D Semantic Labeling Contest - Vaihingen.

The dataset can be requested at the challenge homepage. Or BaiduNetdisk，password：mseg, Google Drive. The 'ISPRS_semantic_labeling_Vaihingen.zip' and 'ISPRS_semantic_labeling_Vaihingen_ground_truth_eroded_COMPLETE.zip' are required.

For Vaihingen dataset, please run the following command to re-organize the dataset.

python tools/dataset_converters/vaihingen.py /path/to/vaihingen

In our default setting (clip_size=512, stride_size=256), it will generate 344 images for training and 398 images for validation.

iSAID

The data images could be download from DOTA-v1.0 (train/val/test)

The data annotations could be download from iSAID (train/val)

The dataset is a Large-scale Dataset for Instance Segmentation (also have semantic segmentation) in Aerial Images.

You may need to follow the following structure for dataset preparation after downloading iSAID dataset.

├── data
│   ├── iSAID
│   │   ├── train
│   │   │   ├── images
│   │   │   │   ├── part1.zip
│   │   │   │   ├── part2.zip
│   │   │   │   ├── part3.zip
│   │   │   ├── Semantic_masks
│   │   │   │   ├── images.zip
│   │   ├── val
│   │   │   ├── images
│   │   │   │   ├── part1.zip
│   │   │   ├── Semantic_masks
│   │   │   │   ├── images.zip
│   │   ├── test
│   │   │   ├── images
│   │   │   │   ├── part1.zip
│   │   │   │   ├── part2.zip

python tools/dataset_converters/isaid.py /path/to/iSAID

In our default setting (patch_width=896, patch_height=896, overlap_area=384), it will generate 33978 images for training and 11644 images for validation.

UAVid

The data could be downloaded from here.

For UAVid dataset, please run the following command to re-organize the dataset.

python tools/dataset_converters/uavid.py /path/to/UAVid

In our default setting (patch_width=1280, patch_height=1080, overlap_area=0).

UDD5

The data could be downloaded from here.

VDD

The data could be downloaded from here.

Building Extraction

WHU Building Dataset (Aerial & SatⅡ)

The data could be downloaded from here or Kaggle.

You only need to convert the original 0-255 labels to 0-1 labels:

label[label < 128] = 0
label[label >= 128] = 1

xBD

The data could be downloaded from here.

You need to convert the original 0-255 labels to 0-1 labels:

python tools/dataset_converters/xBD.py /path/to/xBD

Then, move the images and labels with "pre" in the filename to a new directory.

Inria

The data could be downloaded from here, the data split refers to FCtL.

For Inria dataset, please run the following command to re-organize the dataset.

python tools/dataset_converters/inria.py /path/to/Inria

In our default setting (patch_width=1000, patch_height=1000, overlap_area=0).

Road Extraction

All road datasets need to be converted from the original 0-255 labels to 0-1 labels.

CHN6-CUG Road Dataset

The data could be downloaded from here.

For CHN6-CUG dataset, please run the following command to re-organize the dataset.

python tools/dataset_converters/chn6-cug.py /path/to/CHN6-CUG

DeepGlobe Road Detection

The data could be downloaded from here.

More details about DeepGlobe can be found here.

Massachusetts Roads Dataset

The data could be downloaded from here.

More details about Massachusetts can be found here.

SpaceNet

The data could be downloaded from here.

More details about Massachusetts can be found here.

Water Extraction

water-body-segmentation-in-satellite-images

The data could be downloaded from here.

Use the filenames listed in tools/dataset_converters/wbs-si_val.txt as the validation set.