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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