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Deep learning model to detect change in buildings in high resolution drone or aerial imagery. A brief summary of the item is not available. Add a brief summary about the item.

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Item created: Oct 14, 2024 Item updated: Jan 2, 2025 Number of downloads: 885

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Description

Detecting and monitoring changes in buildings is crucial for urban development, safety, and environmental sustainability. It enables city planners to track growth, ensure compliance with zoning laws, update property records, and detect illegal constructions. Monitoring changes can help assess the impact of urbanization on infrastructure and natural resources, and the impact of any natural disaster on the urban development. Using deep learning models, with high resolution aerial or drone imagery, can greatly improve this process by automating large-scale detection, and identifying subtle changes that human inspectors might miss. This can enhance accuracy, reduce manual labor, and support data-driven urban planning decisions.

Use this model to detect changes in buildings and produce a continuous change magnitude raster instead of a simple binary raster, indicating probability of change at each pixel.


Using the model

Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.

Fine-tuning the model
This model cannot be fine-tuned using ArcGIS tools.

Input
High resolution raster, mosaic dataset, or image service (10 - 20 centimeter spatial resolution) for pre and post time period. The rasters for the pre and post time periods should be accurately aligned with no relief displacement.

Output
Raster representing magnitude of change for each pixel.

Applicable geographies
The model is expected to work well in less densely populated urban areas.

Model architecture
This model is based upon Changen, a GAN-based Generative Probabilistic Change Model (GPCN) by Z. Zheng et. al.

Sample results
Here are a few results from the model.






Citations
  • Zheng, Z., Tian, S., Ma, A., Zhang, L., and Zhong, Y., 2023. Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 21818–21827.
  • Zheng, Z., Ma, A., Zhang, L., and Zhong, Y., 2021. Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 15193–15202.
  • Zheng, Z., Zhong, Y., Wang, J., Ma, A., and Zhang, L., 2020. Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4096–4105.
  • Zheng, Z., Zhong, Y., Wang, J., Ma, A., and Zhang, L., 2023. FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), pp. 13715–13729.

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          Size: 87.498 MB

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          ID: a779106ce9eb4fc28de6d04d8edb893c

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          Pretrained Model, DLPK, Change Detection, Building Change Detection, Deep Learning, LivingAtlasDLPK

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          Esri, Z. Zheng et. al

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