New unfreezing strategy of transfer learning in satellite imagery for mapping the diversity of slum areas: A case study in Kenitra city—Morocco

Abstract

The purpose behind this paper research is to localize and detect, for the first time, three types of shantytowns that can exist in both urban and rural areas in Morocco. The reason why we have conducted this study is the absence of diversity slum mapping in the form of dynamic GIS data, which can be a part of national development strategies to tackle the risk of further proliferation of shantytowns. Therefore, we first create a new database integration from five countries whose content contains 1366 high-resolution patches of 224 × 224 pixels divided into five classes: two types of “Urban Slum”, one type of “Rural Slum”, “Formal Buildings”, and non-built-up area. Second, we suggest employing a variety of performance metrics to come up with the best transfer learning-based models for the unfreezing stage. The study reveals that by handling the unfreezing of the front layers, we can produce a reliable new model where one condition is fulfilled, namely, where high-frequency dissimilarities have arisen between the target and the source datasets.Quantitative results of these methodologies show that our dataset and our unfreezing strategy for the mobilenetv2 model are more accurate compared to other competing models. This new model can localize all possible changes in morphology and shape among the three-slum types with state-of-the-art performance that reaches an overall accuracy of 98.17%, a loss of 0.062, and a kappa score of 97.71%. Our dataset and the code will be publicly available at the github11 https://github.com/mouddentarik/New_Unfreezing_Strategy. to share our results.

Keywords

• Deep learning
• Diversity of slum
• Transfer learning
• Unfreezing strategy
• Remote sensing dataset
• GIS data