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Federated Deep Learning frameworks can be used strategically to monitor LandUse locally and infer environmental impacts globally. Distributed data fromacross the world would be needed to build a global model for Land Useclassification. The need for a Federated approach in this application domainwould be to avoid transfer of data from distributed locations and save networkbandwidth to reduce communication cost. We use a Federated UNet model forSemantic Segmentation of satellite and street view images. The novelty of theproposed architecture is the integration of Knowledge Distillation to reducecommunication cost and response time. The accuracy obtained was above 95% andwe also brought in a significant model compression to over 17 times and 62times for street View and satellite images respectively. Our proposed frameworkhas the potential to be a game-changer in real-time tracking of climate changeacross the planet.

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