# Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation Source: [Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:30:18.740Z Authors: Xianming Liu, Amy Zhang, Tobias Tiecke, Andreas Gros, Thomas S. Huang Published: 2016-12-08T18:46:39.000Z ## Abstract Learning from weakly-supervised data is one of the main challenges in machine learning and computer vision, especially for tasks such as image semantic segmentation where labeling is extremely expensive and subjective. In this paper, we propose a novel neural network architecture to perform weakly-supervised learning by suppressing irrelevant neuron activations. It localizes objects of interest by learning from image-level categorical labels in an end-to-end manner. We apply this algorithm to a practical challenge of transforming satellite images into a map of settlements and individual buildings. Experimental results show that the proposed algorithm achieves superior performance and efficiency when compared with various baseline models. ## 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. ## Original sources and implementation [Original paper]() [PDF]()