Mapping landslides through a temporal lens: an insight toward multi-temporal landslide mapping using the u-net deep learning model. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Bhuyan, Kushanav; Meena, Sansar Raj; Nava, Lorenzo; van Westen, Cees; Floris, Mario; Catani, Filippo
• Is Part Of: GIScience & remote sensing. Volume 60:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Multi-temporal -- deep learning -- U-net -- Himalayan -- landslide inventories; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2023.2182057
• Languages: English
• ISSNs: 1548-1603

Abstract

ABSTRACT: Repeated temporal mapping of landslides is essential for investigating changes in landslide movements, legacy effects of the landslide triggering events, and susceptibility changes in the area. However, in order to perform such investigations, multi-temporal (MT) inventories of landslides are required. The traditional approach of visual interpretation from cloud-free optical remote sensing imageries is time consuming and expensive. Recent endeavors exploring Convolutional Neural Networks and deep learning models have made rapid and accurate mapping of landslides feasible but have not been applied for multi-temporal landslide mapping in the Himalayas, yet. Earlier models used a standard supervised learning approach, with a small landslide inventory over a limited area used for training, which is then utilized to predict landslides in nearby areas. We propose a new strategy, using geographically separate training samples to design a standard approach which can be utilized to create multi-temporal landslide inventories. RapidEye images of 5-meters spatial resolution are used to generate MT landslide inventories in the study area of Rasuwa district, Nepal. We test the effectiveness of the model by training with only 55 landslides and predicting for a different area. Then, using the weights attained from this first training phase, we use transfer learning to map landslides over a time period between 2013 and 2019 in the Rasuwa district attaining an average F1-score of 0.69 for the study area. We also perform a spatial comparison between the manual (observed) and predicted inventories to evaluate the differences between landslide densities and overall landslide statistics of landslide area distribution. The benefit of a transfer learning-based model training is that it circumvents the need for generating annual inventories for training a deep learning. A single event-based inventory is enough to generate landslide inventories over a number of years, at least until landslide conditioning factors do not change significantly. This application can enable automated workflows to generate MT landslide inventories of particular areas as the basis for landslide evolution and movement change analysis.