Detecting Land Abandonment in ?ód? Voivodeship Using Convolutional Neural Networks

Stanisław Krysiak, Elżbieta Papińska, Anna Majchrowska, Maciej Adamiak and Mikołaj Koziarkiewicz
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Stanisław Krysiak: Department of Physical Geography, Faculty of Geographical Sciences, University of Lodz, 90-139 Łódź, Poland
Elżbieta Papińska: Department of Physical Geography, Faculty of Geographical Sciences, University of Lodz, 90-139 Łódź, Poland
Anna Majchrowska: Department of Physical Geography, Faculty of Geographical Sciences, University of Lodz, 90-139 Łódź, Poland
Maciej Adamiak: SoftwareMill, 02-791 Warsaw, Poland
Mikołaj Koziarkiewicz: SoftwareMill, 02-791 Warsaw, Poland

Land, 2020, vol. 9, issue 3, 1-21

Abstract: The wide availability of multispectral satellite imagery through projects such as Landsat and Sentinel, combined with the introduction of deep learning in general and Convolutional Neural Networks (CNNs) in particular, has allowed for the rapid and effective analysis of multiple classes of problems pertaining to land coverage. Taking advantage of the two phenomena, we propose a machine learning model for the classification of land abandonment. We designed a Convolutional Neural Network architecture that outputs a classification probability for the presence of land abandonment in a given 15–25 ha grid element by using multispectral imaging data obtained through Sentinel Hub. For both the training and validation of the model, we used imagery of the ?ód? Voivodeship in central Poland. The main source of truth was a 2009 orthophoto study available from the WMS (Web Map Service) of the Geoportal site. The model achieved 0.855 auc (area under curve), 0.47 loss, and 0.78 accuracy for the test dataset. Using the classification results and the Getis–Ord Gi* statistic, we prepared a map of cold- and hotspots with individual areas that exceed 50 km 2 . This thresholded heatmap allowed for an analysis of contributing factors for both low and intense land abandonment, demonstrating that common trends are identifiable through the interpretation of the classification results of the chosen model. We additionally performed a comparative field study on two selected cold- and hotspots. The study, along with the high-accuracy results of the model’s validation, confirms that CNN-type models are an effective tool for the automatic detection of land abandonment.

Keywords: land cover; GIS; machine learning; convolutional neural networks; ?ód? Voivodeship (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2020
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