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Workflow
Annie edited this page Aug 17, 2018
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labels.geojson: with bounding boxes location, uid, class type (damaged or non-damaged buildings), and tiff id (2048 x 2048 tiff names)
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a folder of tif chips of the size 2048 * 2048. Every chip contains at least one bbox.
- Plot bounding boxes over big tiffs with bbox uids. Manual record the uids of bad labels
- Delete uids of bad labels from geojson, form a new geojson label file
- Split images into training, validation and test data
- Split geojson file to form training label geojson file and test label geojson file
- number of training big chips:
- number of test big chips:
- number of training bboxes for class 1:
- number of training bboxes for class 2:
- number of test bboxes for class 1:
- number of test bboxes for class 2:
- Chip 2048 tif into smaller tifs
- discard black small tifs and their bboxes on the fly for train and val
- automatically removing cloud-covered bboxes on the fly for train and val
- remove small chips that do not contain any bboxes
- Augment damaged buildings various combinations of augmentation techiniques
- convert training and validation images and bboxes to TF-record formats.
- create .pbtxt file as label map (1: damaged buildings, 2: non-damaged buildings)
- Create TF record for test data
- Generate detection results using tensorflow inference code. The results are written in a TF record file
- Compute metrics using TF record file with tensorflow code, see here: https://github.com/tensorflow/models/blob/1f562faacb0f0a083be9bcd7f9e72d94bb23e310/research/object_detection/g3doc/oid_inference_and_evaluation.md
- Piece together detections on small tiffs to make 2048 x 2048 tiff files.
- Plot ground truth and predictions side by side in one image.