University of Twente Student Theses


Detection of Potential Micro Land Grabbing in the Netherlands using Deep Learning

Shi, Shan (2020) Detection of Potential Micro Land Grabbing in the Netherlands using Deep Learning.

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Abstract:In the Netherlands, the behaviour of residents grabbing small pieces of municipal land has attracted the attention of the government for the past ten years. Currently, the Dutch government uses traditional visual inspection methods to find the potential micro land grabbing cases from aerial images. In the long run, this traditional approach is costly, time and labour intensive. To date, this problem has received limited attention. There is some research focusing on the causes and characteristics of its occurrence. Our research aims to propose a novel and efficient method to identify the potential micro land grabbing cases in a complex urban area. We present an automated method, which involves deep convolutional network but also image analysis. This automated method addressing a semantic segmentation problem in an endeavour to distinguish “potential micro land grabbing (PMLG)” pixels from “Non-PMLG” pixels, respectively. However, the visual cues to define a PMLG case directly from the image is too complicated for machine to learn as it also involves land tenure situation. Specifically, the first part of this method develops a SegNet model with overall F-score higher than 0.8. SegNet model extracts land cover features from the imagery automatically and then shows the output in an inference classified map, where each pixel is well predicted into different land cover types, such as buildings, gardens, water area, roads, etc. Based on the outputs created using deep learning, image analysis is performed with the help of official land right information provided by Kadaster. The output of the calculation classifies each pixel into “PMLG” or “Non-PMLG” , which is the final result of the proposed method. The method is also compared with the result of traditional visual inspection in Dutch cadastre. The result from the comparison shows that the proposed automated method finds most of the PMLG cases also outlined from the Kadaster and finds some additional. The results are shown false positive and false negative, respectively. An average of 0.20 precision and 0.54 recall has been achieved. For the IoU score, the class PMLG is 0.16 and Non-PMLG is 0.98, on average 0.57. This research concludes that the proposed automated method is a novel, effective and efficient method that can speed up the currently used from the Kadaster method in identifying PMLG in the Netherlands.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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