Land use classification or landform delineation from remote sensing images using Deep Learning. This repo contains codes for mapping thermokarst landforms including thermo-erosion gullies and retrogressive thaw slumps.
If codes here are useful for your project, please consider citing our papers:
The scripts for producing our results in paper Huang et at. 2020. Please check out the RSE2020paper Branch.
@article{huang2020using,
title={Using Deep Learning to Map Retrogressive Thaw Slumps in the Beiluhe Region (Tibetan Plateau) from CubeSat Images},
author={Huang, Lingcao and Luo, Jing and Lin, Zhanju and Niu, Fujun and Liu, Lin},
journal={Remote Sensing of Environment},
volume = {237},
pages={111534},
year = {2020},
publisher={ELSEVIER},
doi = {https://doi.org/10.1016/j.rse.2019.111534}
}
@article{huang2018automatic,
title={Automatic Mapping of Thermokarst Landforms from Remote Sensing Images Using Deep Learning: A Case Study in the Northeastern Tibetan Plateau},
author={Huang, Lingcao and Liu, Lin and Jiang, Liming and Zhang, Tingjun},
journal={Remote Sensing},
volume={10},
number={12},
pages={2067},
year={2018},
publisher={Multidisciplinary Digital Publishing Institute}
}
See the script: working_dir/exe.sh
Please let me know or pull a request if you spot any bug or typo. Thanks! Any enhancement or new functions are also welcome!
January 2021: Clean or re-organize the scripts; re-define the input parameter files, separate them into main ini, network defined ini, and regions defined ini, making it easy to different regions or networks for training and inference; split data into training (90%) and validation (10%) for checking the overfitting issue. output mIOU during training and allow early stopping.
March 2019: Many scripts for producing figures of manuscript: Using Deep Learning to Map Retrogressive Thaw Slumps in the Beiluhe Region (Tibetan Plateau) from CubeSat Images, Remote sensing of Environment.
January 2019: Supporting of Mask RCNN
August 2018: Delineate retrogressive thaw slumps from Planet CubeSat images.
March 2018: Land use classification using the data from 2018_IEEE_GRSS_Data_Fusion. Also submitted the result. codes in "grss_data_fusion". The method utilized Deeplab V4(+3), a semantic segmentation algorithm, to classify land use (20 classes).
Python package: Numpy, rasterio, GDAL 2.3, tensorflow-gpu 1.6, pyshp 1.2.12, pillow, imgaug
Other: GDAL, OTB, ASP, CUDA 9.0, cudnn 7.0.
More information on the setting can be found in 'docker_ubuntu1604/run_INsingularity_hlctest.sh' and 'tutorial/Setting_running_on_ITSC.md'
This is a personal repo that we are actively developing. It may not work as expected. We have all the settings on our workstations and servers. You need to spend some efforts on environment settings on your computers before running these codes. These codes are only for research, and please take risks by yourself.
We will update some of the codes, including bug fix and enhancement because they are used in other projects.
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