Deep Learning Application for Urban Change Detection from Aerial Images

Fyleris, Tautvydas; Kriščiūnas, Andrius; Gružauskas, Valentas; Čalnerytė, Dalia

doi:10.5220/0010415700150024

Cited by 4 publications

(1 citation statement)

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“…Satellite images were segmented and classified, such as wetland, water, agriculture, and urban/suburban regions. Using the OpenStreetMap (OSM) database, a few existing research works [19,20] have demonstrated the classification of land use categories, such as water, road, and agricultural, with seasonal and climate-related changes. Time-series analysis is also relevant to aerial/satellite image analysis, with algorithms, such as dynamic time warping (DTM) filling in temporal gaps in the remote sensing time-series data [21] and helping to overcome the limitation of irregularly distributed training samples.…”

Section: Lulc Detection From Satellite Imagesmentioning

confidence: 99%

FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views

et al. 2023

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Federated deep learning frameworks can be used strategically to monitor land use locally and infer environmental impacts globally. Distributed data from across the world would be needed to build a global model for land use classification. The need for a federated approach in this application domain would be to avoid the transfer of data from distributed locations and save network bandwidth to reduce communication costs. We used a federated UNet model for the semantic segmentation of satellite and street view images. The novelty of the proposed architecture involves the integration of knowledge distillation to reduce communication costs and response times. The accuracy obtained was above 95% and we also brought in a significant model compression to over 17 times and 62 times for street-view and satellite images, respectively. Our proposed framework has the potential to significantly improve the efficiency and privacy of real-time tracking of climate change across the planet.

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Section: Lulc Detection From Satellite Imagesmentioning

confidence: 99%

FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views

et al. 2023

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Change Detection of Terrain Using Satellite Imagery

Shah,

et al. 2023

2023 International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS

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Urbanization Detection Using LiDAR-Based Remote Sensing Images of Azad Kashmir Using Novel 3D CNNs

et al. 2022

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An important measurable indicator of urbanization and its environmental implications has been identified as the urban impervious surface. It presents a strategy based on three-dimensional convolutional neural networks (3D CNNs) for extracting urbanization from the LiDAR datasets using deep learning technology. Various 3D CNN parameters are tested to see how they affect impervious surface extraction. For urban impervious surface delineation, this study investigates the synergistic integration of multiple remote sensing datasets of Azad Kashmir, State of Pakistan, to alleviate the restrictions imposed by single sensor data. Overall accuracy was greater than 95% and overall kappa value was greater than 90% in our suggested 3D CNN approach, which shows tremendous promise for impervious surface extraction. Because it uses multiscale convolutional processes to combine spatial and spectral information and texture and feature maps, we discovered that our proposed 3D CNN approach makes better use of urbanization than the commonly utilized pixel-based support vector machine classifier. In the fast-growing big data era, image analysis presents significant obstacles, yet our proposed 3D CNNs will effectively extract more urban impervious surfaces.

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Deep Learning Application for Urban Change Detection from Aerial Images

Cited by 4 publications

References 0 publications

FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views

FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views

Change Detection of Terrain Using Satellite Imagery

Urbanization Detection Using LiDAR-Based Remote Sensing Images of Azad Kashmir Using Novel 3D CNNs

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