# Improved Knowledge Distillation for Land-Use Image Classification Source: [Improved Knowledge Distillation for Land-Use Image Classification]() Collection index: [llms.txt]() Data updated: 2026-06-17T05:00:05.807Z Authors: Arundhuti Sur, Abhiroop Chatterjee, Susmita Ghosh, Emmett Ientilucci Published: 2026-06-12T18:47:12.000Z ## Abstract In the present article, an improved Knowledge Distillation (KD) framework has been proposed for efficient compression of deep convolutional neural networks for land-use image classification task. Motivated by the need to achieve competitive classification accuracy while reducing computational complexity, a teacher-student learning paradigm is adopted in which a VGG16 network transfers knowledge to a lightweight MobileNetV2 model. The proposed framework integrates hard supervision from ground truth labels with a soft supervision strategy that combines Kullback-Leibler divergence and Cosine Similarity losses. Experiments conducted on three land-use datasets show that the proposed KD-based method yields improved performance, and achieves an accuracy of 99.04%, outperforming both baseline student training and single-loss distillation approaches, while retaining substantial model compression. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on UC Merced (test)](): Image Classification; UC Merced (test); Accuracy: 99.04. ## Original sources and implementation [Original paper]() [PDF]()