Beyond output-mask comparison: A self-supervised inspired object scoring system for building change detection

Are C Jensen
Proceedings of the 5th Northern Lights Deep Learning Conference ({NLDL}), PMLR 233:97-103, 2024.

Abstract

Updating urban-area maps is crucial for urban planning and development. Traditional methods of updating urban-area maps based on aerial photography are labor-intensive and struggle to keep pace with rapid urban development. Automated algorithms for detecting new and removed buildings based on bi-temporal images typically either rely on comparing mono-temporal building detection outputs or requiring examples of new and removed buildings for training. This study presents a novel method using self-supervised learning principles to train a distinct object-change scoring network. It repurposes segments of the (potentially imperfect) delineations used in single-temporal detector training, harnesses bi-temporal data attributes, and leverages the assumption that most buildings remain unchanged over time. This eliminates the need for explicit examples of new or removed buildings, while still overcome usual constraints of post-detection output-mask comparison methods. We provide precision-recall curves and examples demonstrating the improved performance of the suggested approach. Furthermore, we discuss several immediate algorithmic variations that hold the potential for even further enhancements in performance.

Cite this Paper

BibTeX
@InProceedings{pmlr-v233-jensen24a, title = {Beyond output-mask comparison: A self-supervised inspired object scoring system for building change detection}, author = {Jensen, Are C}, booktitle = {Proceedings of the 5th Northern Lights Deep Learning Conference ({NLDL})}, pages = {97--103}, year = {2024}, editor = {Lutchyn, Tetiana and Ramírez Rivera, Adín and Ricaud, Benjamin}, volume = {233}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v233/jensen24a/jensen24a.pdf}, url = {https://proceedings.mlr.press/v233/jensen24a.html}, abstract = {Updating urban-area maps is crucial for urban planning and development. Traditional methods of updating urban-area maps based on aerial photography are labor-intensive and struggle to keep pace with rapid urban development. Automated algorithms for detecting new and removed buildings based on bi-temporal images typically either rely on comparing mono-temporal building detection outputs or requiring examples of new and removed buildings for training. This study presents a novel method using self-supervised learning principles to train a distinct object-change scoring network. It repurposes segments of the (potentially imperfect) delineations used in single-temporal detector training, harnesses bi-temporal data attributes, and leverages the assumption that most buildings remain unchanged over time. This eliminates the need for explicit examples of new or removed buildings, while still overcome usual constraints of post-detection output-mask comparison methods. We provide precision-recall curves and examples demonstrating the improved performance of the suggested approach. Furthermore, we discuss several immediate algorithmic variations that hold the potential for even further enhancements in performance.} }
Endnote
%0 Conference Paper %T Beyond output-mask comparison: A self-supervised inspired object scoring system for building change detection %A Are C Jensen %B Proceedings of the 5th Northern Lights Deep Learning Conference ({NLDL}) %C Proceedings of Machine Learning Research %D 2024 %E Tetiana Lutchyn %E Adín Ramírez Rivera %E Benjamin Ricaud %F pmlr-v233-jensen24a %I PMLR %P 97--103 %U https://proceedings.mlr.press/v233/jensen24a.html %V 233 %X Updating urban-area maps is crucial for urban planning and development. Traditional methods of updating urban-area maps based on aerial photography are labor-intensive and struggle to keep pace with rapid urban development. Automated algorithms for detecting new and removed buildings based on bi-temporal images typically either rely on comparing mono-temporal building detection outputs or requiring examples of new and removed buildings for training. This study presents a novel method using self-supervised learning principles to train a distinct object-change scoring network. It repurposes segments of the (potentially imperfect) delineations used in single-temporal detector training, harnesses bi-temporal data attributes, and leverages the assumption that most buildings remain unchanged over time. This eliminates the need for explicit examples of new or removed buildings, while still overcome usual constraints of post-detection output-mask comparison methods. We provide precision-recall curves and examples demonstrating the improved performance of the suggested approach. Furthermore, we discuss several immediate algorithmic variations that hold the potential for even further enhancements in performance.
APA
Jensen, A.C.. (2024). Beyond output-mask comparison: A self-supervised inspired object scoring system for building change detection. Proceedings of the 5th Northern Lights Deep Learning Conference ({NLDL}), in Proceedings of Machine Learning Research 233:97-103 Available from https://proceedings.mlr.press/v233/jensen24a.html.

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