2021-09-30, 09:00–09:30, Academic
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.
Landslides are global natural hazards which are directly affecting lives and environment, as well as various economical aspects. The importance for mapping and monitoring, whether through ground-,air- or spaceborne techniques, landslide-prone areas and already known ones is highlighted in numerous studies, and integrated in many risk mitigation strategies. However, in the recent years several tendencies emerged from separate fields that tend to unite according to the research problem.
On one hand, Earth Observation (EO) free and open-source datasets and techniques naturally blended into Geographical Information Systems (GIS) and have found new aspects of implementation (e.g. disaster mapping, land cover changes, etc.).
On the other hand, Volunteered Geographic Information (VGI) emerged and proved as invaluable data source also in the disaster response domain. There are many practical examples of using OpenStreetMap data and volunteering collaborative mapping for risk management, relief and recovery strategies. The fusion between these different data gathering and processing methods provided information from diverse aspects and contribution even to the landslide studies.
Using EO, whether it is from optical or radar air/spaceborne sensing platforms, provided applications for detecting and mapping slope failures, even in monitoring their activity state or slow displacements, at a millimetre precision.
In addition, the use in the recent years of Artificial Intelligence (AI) and especially Machine Learning (ML) approaches, has increased in the field of Earth Observations. ML has been used for image classification applications, cloud detection and removal, enhancing the spatial resolution of satellite imagery, and many more. Naturally, scholars and decision-makers adopted machine learning techniques in their workflows and strategies for processing remotely sensed data in geohazard studies. Such implementations are already applied for landslide detection and mapping, landslide susceptibility and hazard mapping.
Even though, crowd-sourced data collection campaigns are often used in the disaster domain, both for risk mapping and disaster response, there are very few landslide-specific platforms and applications currently operational and well-known. In the paper, we will present the very few desktop and mobile-based applications and catalogues for collecting landslide-related geospatial -information. Lastly, it will be presented and discussed some current applications of AI on VGI datasets for recognition and detection of landslides. Moreover, it will be discussed how UAV datasets could be also obtained in a citizen science manner through VGI collaborative platforms.
The paper will not only discuss the geoinformatics state-of-the-art landslide mapping and monitoring techniques but will also highlight the importance of combination and contribution between the different domains because hardly any risk related problem is a single-aspect one. Moreover, bringing expertise from fields is a must in the hazard domain. Interdisciplinary approaches are not only bringing deeper understand of the problem but will be improving methodologies that are, and will be, yielding more accurate and time/cost efficient results for better mitigating the landslide hazard.
Yordanov, Vasil (1,2)
Biagi, Ludovico (1)
Truong, Xuan Quang (3)
Tran, Van Anh (4)
Brovelli, Maria Antonia (1,5)
(1)Department of Civil and Environmental Engineering (DICA) Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, Italy - (vasil.yordanov, maria.brovelli, ludovico.biagi)@polimi.it
(2)Vasil Levski National Military University, Veliko Tarnovo, Bulgaria
(3)Information Technology Faculty, Hanoi University of Natural Resources and Environment, 41A Phu Dien Road, Phu Dien, North-Tu Liem district, Hanoi, Vietnam - firstname.lastname@example.org
(4)Dept. of Geomatics and Land Administration, HUMG, HaNoi University of Mining and Geology, 18 Vien Street, Bac Tu Liem, Hanoi, Vietnam- email@example.com
(5)Istituto per il Rilevamento Elettromagnetico dell’Ambiente, CNR-IREA, via Bassini 15, 20133 Milano – firstname.lastname@example.org
2 - Basic. General basic knowledge is required.Language of the Presentation –
Vasil Yordanov obtained his MSc degree in Civil Engineering for Risk Mitigation at Politecnico di Milano in 2016. He joined the GEOlab team of Politecnico di Milano as a temporary research fellow in April 2019. His main research interests are hazard-related remote sensing applications and data analysis.