In order to understand the changing trend of the number of airplanes as an index of the airport activity, here applied a method that utilizes convolutional neural networks to effectively detect airplane in Sentinel-2 images. From the detection, we successfully obtained the changing trend of the number of airplanes in important airports around the world since the outbreak of COVID-19, and found different changing trends in different areas that may reflect different reactions to COIVD-19 situation in each country. Paper available open access at: https://igarss2021.com/view_paper.php?PaperNum=1248
In this paper, we use the Rareplanes dataset as the training set. Available Open access at: https://github.com/aireveries/RarePlanes
The data used to detect the image uses data taken by the Sentinel-2 satellite. Available Open access at: https://github.com/sentinel-hub/sentinelhub-py
- This article uses VOC format for training.
- Before training, put the label file in the Annotation under the VOC2007 folder under the VOCdevkit folder.
- Before training, place the picture files in the JPEGImages under the VOC2007 folder under the VOCdevkit folder.
- Use the voc2yolo4.py file to generate the corresponding txt before training.
- Run voc_annotation.py in the root directory again, you need to change the classes to your own classes before running.
- At this time, the corresponding 2007_train.txt will be generated, and each line corresponds to the position of the picture and the position of the real frame.
- Run train.py to start training.
A sample of the result is provided at in ./sample