Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks
Open accessAbstract
The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate. In this paper we explore the usage of convolutional neural networks for urban change detection using such multispectral images. We first present the new change detection dataset that was used for training the proposed networks, which will be openly available to serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is composed of pairs of multispectral aerial images, and the changes were manually annotated at pixel level. We then propose two architectures to detect changes, Siamese and Early Fusion, and compare the impact of using different numbers of spectral channels as inputs. These architectures are trained from scratch using the provided dataset.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The trained fine-grain quantum classifier was applied to unseen Sentinel 2 images from the Onera Satellite Change Detection Dataset (OSCD).
“the trained fine-grain quantum classifier has eventually been applied to unseen Sentinel 2 images from the Onera Satellite Change Detection Dataset (OSCD) [79].”
From On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification (2022) · cites this paper as [79]
Cited by
Sorted by citations
Guo, X., Lao, J., Dang, B., Zhang, Y., Yu, L., Ru, L., Zhong, L., Huang, Z., Wu, K., Hu, D., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02613 · Cited by 290 · see citing works
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID
Long, Y., Xia, G.-S., Li, S., Yang, W., Yang, M. Y., Zhu, X. X., Zhang, L., & Li, D. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3070368 · Cited by 250 · see citing works
On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification
Sebastianelli, A., Zaidenberg, D. A., Spiller, D., Le Saux, B., & Ullo, S. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3134785 · Cited by 162 · see citing works
Self-citationSatellite On-Board Change Detection via Auto-Associative Neural Networks
Guerrisi, G., Del Frate, F., & Schiavon, G. (2022) · Remote Sensing · doi:10.3390/rs14122735 · Cited by 8 · see citing works
- Authors
- Daudt, R. C., Le Saux, B., Boulch, A., & Gousseau, Y.
- Affiliations
- Université Paris-Saclay (FR)
- DTIS, ONERA, Université Paris-Saclay, Palaiseau, FR-91123, France
- Office National d'Études et de Recherches Aérospatiales (FR)
- Télécom Paris (FR)
- Venue
- IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 2115-2118
- OpenAlex
- W2896365540
- Cited by
- 466
- References
- 0
- Citations by year
- 2026: 23 · 2025: 78 · 2024: 88 · 2023: 86 · 2022: 62 · 2021: 62 · 2020: 40 · 2019: 23 · 2018: 4
- FWCI
- 53.12
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Remote Sensing and Land Use
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- urban change detection, Sentinel-2, multispectral imagery, convolutional neural networks, Siamese network, feature-level fusion, aerial imagery
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
