HumAI-DisplacementMonitoring/CDSI

• Paper:: arXiv:2505.22353

Environment Setup for data preparation and preprocessing

pip install -r requirements.txt

CDSI Dataset preparation

For using the VME dataset, both OBB (YOLO) and HBB (MS-COCO) formats are available with the desired splits. VME images and annotations are available at XXX.

In order to build the CDSI dataset, you need to follow the steps below:

1. Download each dataset from its repository:

   • xView (https://challenge.xviewdataset.org/)
   • DOTA-v2.0 (https://captain-whu.github.io/DOTA/index.html)
   • VEDAI (https://downloads.greyc.fr/vedai/)
   • DIOR (https://gcheng-nwpu.github.io/)
   • FAIR1M-2.0 (http://gaofen-challenge.com/benchmark)

2. Convert each dataset to MS-COCO (JSON) format with original/ car-related + other small objects(car-other)/ car-related (car) setup, utilizing each dataset scripts under main directory:

Instructions before starting:

a. After installing some datasets, it's recommended to put all images under one directory and all annotations under one directory, to ease the MS-COCO conversion

b. To generate SPLIT_FILE.txt for each setup (original|CDSI_other|CDSI), run this command under each dataset directory (example for car setup):

grep 'xview' CDSI_train.txt > xview_car_train.txt

SPLIT_FILE == xview_car_train.txt

** For DOTA-v2.0: please check dota2 directory

Converting each dataset to COCO format

Under each dataset directory, DATANAME_to_coco.py script is available to convert the specified dataset

python DATANAME_to_coco.py IMG_DIR SPLIT_FILE.txt OUT_DIR MODE

Combining train-val-test splits

To combine the train-val-test splits from each dataset to construct CDSI splits, please use the following command/package. The command takes 3 arguments:File1.json File2.json OutputFile.json. T$

pyodi coco merge SPLIT_A.json SPLIT_B.json SPLIT_A_B.json