Roof material classification from aerial imagery
This work addresses a domain-specific problem for urban planning or disaster assessment, but it is incremental as it builds on existing neural network techniques.
The paper tackles roof material classification from aerial imagery by proposing a complete flow with methods to improve prediction accuracy, achieving second place in the 'Open AI Caribbean Challenge'.
This paper describes an algorithm for classification of roof materials using aerial photographs. Main advantages of the algorithm are proposed methods to improve prediction accuracy. Proposed methods includes: method of converting ImageNet weights of neural networks for using multi-channel images; special set of features of second level models that are used in addition to specific predictions of neural networks; special set of image augmentations that improve training accuracy. In addition, complete flow for solving this problem is proposed. The following content is available in open access: solution code, weight sets and architecture of the used neural networks. The proposed solution achieved second place in the competition "Open AI Caribbean Challenge".