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Assessing the spatial transferability of fully convolutional networks for slum mapping

Gao, Yunya (2020) Assessing the spatial transferability of fully convolutional networks for slum mapping.

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Abstract:To fight against poverty, the United Nations have made slum upgrading an important task within the Sustainable Development Goals 11. In support of this target, slum maps providing information about slum spatial location and extent are, thus, significant. In the past few decades, remote sensing (RS) based slum mapping approaches have been developed fast. However, due to the complexity of slums in terms of morphological characteristics, definitions, dynamics and the existence of multiple satellite sensors, transferability has become one of the biggest challenges for RS based slum maping approaches. Based on existing researches, Fully Convolutional Networks (FCNs) have been proved to produce relatively high accuracies for slum mapping compared to other approaches. Existing studies have shown that FCNs are capable of transferring learnt features across different sensors and perform well when tested on the same place at different periods. However, very few studies tested the performance of FCNs when applied to different geographic contexts. For this reason, this research aims to assess the spatial transferability of FCNs for slum mapping. This research selected Mumbai, Nairobi and Rio de Janeiro (Rio) as study areas whose slums are various in terms of morphological characteristics and conceptualizations. This research designed a systematic assessment framework for the spatial transferability of FCNs for slum mapping. The framework includes three dimensions: (i) what are the differences in selection of FCN architecture and hyperparameter setting to reach optimal performance for different spatial contexts, (ii) whether the model trained on data from one source study area and tested on the corresponding study area performs similarly, and (iii) whether the performance of the model pre-trained on data from one source study area and tested on data from a different place is similar. The selection process of hyperparameter setting for a certain FCN architecture is time-consuming and complex. Due to time limitation, this research explored the second and third dimension of the spatial transferability. Furthermore, this research analysed the influences of three adaptations in training strategies on the performance of the FCN model. Adaptation 1 applies fine-tuning before using the model trained on one source study area to predict slums from a different study area. Adaptation 2 uses training data from multiple study areas rather than only one source study area to train the FCN model. Adpation 3 applies fine-tuning before using the model trained on data from multiple source study areas to predict slums in a different study or one of the selected study areas for training. The results revealed that the second dimension of the spatial transferability is low. The performance of the FCN model varied when applied to Mumbai (IoU (Intersection over Union) =65.09%), Nairobi (IoU=43.39%) and Rio (IoU=31.42%). The differences in accuracies are mainly caused by different levels of diversity within slums and similarity between slums and non-slums in different study areas. Slums in Mumbai are more homogenous and distinctive from non-slums compared to Nairobi and Rio. Slums in Rio are more heterogeneous and similar to non-slums. Besides, different reference data collections approaches may also influence the performance of FCN models. Slum reference data for Mumbai and Nairobi are image-based, which means slums are mainly determined based on morphological characteristics reflected on satellite imagery and thus may be easier to be detected by RS based approaches. Slum reference data for Rio are ground-based, where slums are determined by morphological, social and economical characteristics. This means it is harder to detect slums in Rio by RS-based appraoches due to incapability of recognizing social and economical characteristics of slums directly from satellite imagery. Besides, slum reference data for Rio includes some non-slums due to data aggragation. For these reasons, the model trained on Mumbai data performs best while the model trained on Rio data performs worst. For the third dimension, spatial transferability of FCN models is also low. This is mainly because slum morphological characteristics in Mumbai, Nairobi and Rio are different. Therefore, learnt features from the model trained on data from one of the cities are not effective for detecting slums in other cities. Adaptation 1 makes the FCN model trained on data from one source study area perform similarly when predicting slums in a different study area compared to the model trained on the predicted study area with lower computational cost. It can help improve the third dimension of the spatial transferability but cannot improve the second dimension. The performance of adaptation 3 is similar to the adaptation 1. Both adaptation 1 and 3 perform a bit worse than the adaptation 2. Adaptation 2 helps the FCN model perform better than the model trained on one source study area and tested on the same study area. The results indicate that combining training data from multiple source study areas with different slum characteristics can help improve the performance of the FCN model in both the second and the third dimensions of the spatial transferability. Therefore, adaptation 2 may have potentials to help FCN models map slums at large scales and produce comparable or even higher accuracies as FCN models trained on only one source study area and tested on the same study area.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Spatial Engineering MSc (60962)
Link to this item:https://purl.utwente.nl/essays/84935
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