The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLVI-4/W2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W2-2021, 205–212, 2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W2-2021, 205–212, 2021

  19 Aug 2021

19 Aug 2021


V. Yordanov1,2, L. Biagi1, X. Q. Truong3, V. A. Tran4, and M. A. Brovelli1,5 V. Yordanov et al.
  • 1Department of Civil and Environmental Engineering (DICA), Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, Italy
  • 2Vasil Levski National Military University, Veliko Tarnovo, Bulgaria
  • 3Information Technology Faculty, Hanoi University of Natural Resources and Environment, 41A Phu Dien Road, Phu Dien, North-Tu Liem district, Hanoi, Vietnam
  • 4Dept. of Geomatics and Land Administration, HUMG, HaNoi University of Mining and Geology, 18 Vien Street, Bac Tu Liem, Hanoi, Vietnam
  • 5Istituto per il Rilevamento Elettromagnetico dell’Ambiente, CNR-IREA, via Bassini 15, 20133 Milano, Italy

Keywords: Landslides, Earth Observation, Artificial Intelligence, Citizen Science, Detection, Monitoring, Free and open-source approaches

Abstract. Natural hazards such as landslides, whether they are driven by meteorologic or seismic processes, are constantly shaping Earth’s surface. In large percentage of the slope failures, they are also causing huge human and economic losses. As the problem is complex in its nature, proper mitigation and prevention strategies are not straightforward to implement. One important step in the correct direction is the integration of different fields; as such, in this work, we are providing a general overview of approaches and techniques which are adopted and integrated for landslide monitoring and mapping, as both activities are important in the risk prevention strategies. Detailed landslide inventory is important for providing the correct information of the phenomena suitable for further modelling, analysing and implementing suitable mitigation measures. On the other hand, timely monitoring of active landslides could provide priceless insights which can be sufficient for reducing damages. Therefore, in this work popular methods are discussed that use remotely-sensed datasets with a particular focus on the implementation of machine learning into landslide detection, susceptibility modelling and its implementation in early-warning systems. Moreover, it is reviewed how Citizen Science is adopted by scholars for providing valuable landslide-specific information, as well as couple of well-known platforms for Volunteered Geographic Information which have the potential to contribute and be used also in the landslide studies. In addition to proving an overview of the most popular techniques, this paper aims to highlight the importance of implementing interdisciplinary approaches.