Thomas BlaschkeUniversity of Salzburg · Department of Geoinformatics
Thomas Blaschke
PhD Geography / Geoinformatics
About
549
Publications
340,645
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30,945
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Introduction
Additional affiliations
October 2014 - December 2014
August 2006 - December 2006
California State University Long Beach
Position
- Fulbright Professor
May 2006 - present
Education
March 1992 - June 1995
Publications
Publications (549)
This paper presents an automated classification system of landform elements based on object-oriented image analysis. First, several data layers are produced from Digital Terrain Models (DTM): elevation, profile curvature, plan curvature and slope gradient. Second, relatively homogenous objects are delineated at several levels through image segmenta...
In this research, we analyzed the delivery service areas of restaurants, customer satisfaction, and restaurant sales of urban restaurants during the COVID-19 pandemic. We obtained the datasets on food ordering options and restaurant rankings based on Google Maps, Open Street Map, and widely known online food order applications in Iran. Based on thi...
Urban corridors are – from a spatial perspective – massively large, linear urban agglomerations consisting of a number of big cities or clusters aligned along high-speed road or rail lines. Fixed administrative boundaries are commonly used to define such urban areas. However, this does not usually reflect the actual extent of the built-up space in...
Glaciers are generally believed to be subjugating by global warming but the Karakoram glaciers are reportedly maintaining their balance. Earlier studies in the Karakoram and its sub-basins have mostly addressed a short span of time and used complex models to understand the phenomenon. Thus, this study is based on a long-term trend analysis of the c...
Identifying the potential of ecotourism sustainability is one of the priorities of many countries, it is a goal for effective and efficient resource use on earth. Analyzing the growth of the economy and conservation methods for sustainable developing countries can be achieved by determining the possibility of ecotourism sustainability. And, it is e...
In many parts of the world, lake drying is caused by water management failures, while the phenomenon is exacerbated by climate change. Lake Urmia in Northern Iran is drying up at such an alarming rate that it is considered to be a dying lake, which has dire consequences for the whole region. While salinization caused by a dying lake is well underst...
Recent improvements in earth observation technologies and Geographical Information System (GIS) based spatial analysis methods require us to examine the efficiency of the different data-driven methods and decision rules for soil salinity monitoring and degradation mapping. The main objective of this study was to analyze the environmental impacts of...
The main aim of the present study was to investigate the spatiotemporal trends of urban traffic accident hotspots during the COVID-19 pandemic. The severity index was used to determine high-risk areas, and the kernel density estimation method was used to identify the risk of traffic accident hotspots. Accident data for the time period of April 2018...
Classification is a very common image processing task. The accuracy of the classified map is typically assessed through a comparison with real-world situations or with available reference data to estimate the reliability of the classification results. Common accuracy assessment approaches are based on an error matrix and provide a measure for the o...
This work evaluates the performance of three machine learning (ML) techniques, namely
logistic regression (LGR), linear regression (LR), and support vector machines (SVM), and two multi-criteria decision-making (MCDM) techniques, namely analytical hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOP...
In this paper, we aim to compare the suitability of Sentinel-2 and Landsat 8 OLI images for detecting and mapping soil salinity distribution (SSD) using a deep learning convolutional neural network (DL-CNN) approach. We first identified and selected six SSD predisposing variables to train the models. These variables are the normalized difference ve...
In this paper, we aim to compare the suitability of Sentinel-2 and Landsat 8 OLI images for detecting and mapping soil salinity distribution (SSD) using a deep learning convolutional neural network (DL-CNN) approach. We first identified and selected six SSD predisposing variables to train the models. These variables are the normalized difference ve...
In many parts of the world, lake drying is caused by water management failures, while the phenomenon is exacerbated by climate change. Lake Urmia in Northern Iran is drying up at such an alarming rate that it is considered to be a dying lake, which has dire consequences for the whole region. While salinization caused by a dying lake is well underst...
This study deals with the issue of greenwashing, i.e. the false portrayal of companies as environmentally friendly. The analysis focuses on the US metal industry, which is a major emission source of sulfur dioxide (SO2), one of the most harmful air pollutants. One way to monitor the distribution of atmospheric SO2 concentrations is through satellit...
Keywords: Karst zones and landforms Object-based image analysis Spatial and spectral features A semi-automated approach a b s t r a c t This study presents a novel, semi-automated approach for integrating decision rules and object-based image analysis (OBIA) methods for identifying and mapping karst zones and landforms. We developed a multi-resolut...
This study investigates a pixel-based image analysis methodology built on unsupervised Deep Learning (DL) for rapid landslide detection. The utilized data includes the Minimum Noise Fraction (MNF) and Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 images and the topographic slope factor derived from the ALOS PALSAR sensor. We...
During the conflict and in the years afterward, the Kurdistan Region of Iraq (KRI) saw substantial changes in land use. The mapping and monitoring of land use/land cover (LULC) is critical for its long-term development and natural resource management. Therefore in this study, we developed a semi-automated object-based land use/land cover classifica...
The effects of the spatial resolution of remote sensing (RS) data on wildfire susceptibility prediction are not fully understood. In this study, we evaluate the effects of coarse (Landsat 8 and SRTM) and medium (Sentinel-2 and ALOS) spatial resolution data on wildfire susceptibility prediction using random forest (RF) and support vector machine (SV...
Recent landslide detection studies have focused on pixel-based deep learning (DL) approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on distinct features rather than individual pixels. This study examines the feasibility of the integration framework of a DL model with rule-based object-based image analysis (...
The increased use of mobile maps in our highly mobile digital culture has resulted in a large variety of map users and map use situations. For mobile map applications that engage a broad user base and feature diverging map usage contexts, one-size-fits-all map interface designs might result in significant usability tradeoffs. To respond to this cha...
Global urbanization significantly impacts the thermal environment in urban areas, yet urban heat island (UHI) and urban heat wave (UHW) studies at the mega–region scale have been rare, and the impact study of urbanization is still lacking. In this study, the MODIS land surface temperature (LST) product was used to depict the UHI and UHW in nine meg...
Urmia Lake in Northern Iran is drying up, which is causing significant environmental problems in the region, including saline storms that devastate agricultural land. We developed a remote sensing-based monitoring application to detect and map the location of saline flow sources with a novel automated deep learning convolutional neural network (DL-...
Recent improvements in the spatial, temporal, and spectral resolution of satellite images necessitate (semi-)automated classification and information extraction approaches. Therefore, we developed an integrated fuzzy object-based image analysis and deep learning (FOBIA-DL) approach for monitoring the land use/cover (LULC) and respective changes and...
With the recent advances in earth observation technologies, the increasing availability of data from more and more different satellite sensors as well as progress in semi-automated and automated classification techniques enable the (semi-) automated remote monitoring and analysis of large areas. Online platforms such as Google Earth Engine (GEE) br...
With the recent advances in earth observation technologies, the increasing availability of data from more and more different satellite sensors as well as progress in semi-automated and automated classification techniques enable the (semi-) automated remote monitoring and analysis of large areas. Online platforms such as Google Earth Engine (GEE) br...
This paper proposes a new approach based on an unsupervised deep learning (DL) model for landslide detection. Recently, supervised DL models using convolutional neural networks (CNN) have been widely studied for landslide detection. Even though these models provide robust performance and reliable results, they depend highly on a large labeled datas...
In many parts of the world, lake drying is caused by water management failures, while the phenomenon is exacerbated by climate change. Lake Urmia in Northern Iran is drying up at such an alarming rate that it is considered to be a dying lake, which has dire consequences for the whole region. While salinization caused by a dying lake is well underst...
In the context of climate change and urban heat islands, the concept of local climate zones (LCZ) aims for consistent and comparable mapping of urban surface structure and cover across cities. This study provides a timely survey of remote sensing-based applications of LCZ mapping considering the recent increase in publications. We analyze and evalu...
In 2015, the United Nations defined an ambitious mission called “Transforming Our World: the 2030 Agenda for Sustainable Development.” The aims are to transform our world toward prosperity and to ensure well‐being for all while protecting the environment. Seventeen major sustainable development goals (SDGs) are to be achieved through 169 targets me...
Earthquakes and heavy rainfalls are the two leading causes of landslides around the world. Since they often occur across large areas, landslide detection requires rapid and reliable automatic detection approaches. Currently, deep learning (DL) approaches, especially different convolutional neural network and fully convolutional network (FCN) algori...
Urmia Lake in Northern Iran is drying up, which is causing significant environmental problems in the region,
including saline storms that devastate agricultural land. We developed a remote sensing-based monitoring
application to detect and map the location of saline flow sources with a novel automated deep learning convolutional
neural network (DL-...
https://gispoint.de/liste-artikel/agit/ausgabe-7-2021.html
GI_Forum 2021 Journal, Volume 9, Issue 1
Open Access at https://austriaca.at/giscience2021_01_
Special Edition:
12th International Symposium on Digital Earth: Digital Earth for Sustainable Societies
Thomas Blaschke - Josef Strobl - Julia Wegmayr (Eds.)
The world's poorest countries were hit hardest by COVID-19 due to their limited capacities to combat the pandemic. The urban water supply and water consumption are affected by the pandemic because it intensified the existing deficits in the urban water supply and sanitation services. In this study, we develop an integrated spatial analysis approach...
In this study, ASTER imagery, geochemical, lithological, and structural data are exploited for Mineral Potential Mapping (MPM) of the Astaneh granitic pluton and its surrounding area. The independent component analysis (ICA) and Matched Filtering (MF) techniques are applied to ASTER data for detecting alteration mineral assemblages. Sericitically a...
Global sensitivity analysis, like variance-based methods for massive raster datasets, is especially computationally costly and memory-intensive, limiting its applicability for commodity cluster computing. The computational effort depends mainly on the number of model runs, the spatial, spectral, and temporal resolutions, the number of criterion map...
Land subsidence (LS) in arid and semi-arid areas, such as Iran, is a significant threat to sustainable land management. The purpose of this study is to predict the LS distribution by generating land subsidence susceptibility models (LSSMs) for the Shahroud plain in Iran using three different multi-criteria decision making (MCDM) and five different...
The concept of leveraging the predictive capacity of predisposing factors for landslide susceptibility (LS) modeling has been continuously improved in recent work focusing on computational and machine learning algorithms. This paper explores the predictive capacity of different approaches to LS modelling using artificial intelligence. The key objec...
Traditional soil salinity studies are time-consuming and expensive, especially over large areas. This study proposed an innovative deep learning convolutional neural network (DL-CNN) data-driven approach for SSD mapping. Multi-spectral remote sensing data encompassing Landsat series images provide the possibility for frequent assessment of SSD in v...
The morphological characteristics of yardangs are the direct evidence that reveals the wind and fluvial erosion for lacustrine sediments in arid areas. These features can be critical indicators in reconstructing local wind directions and environment conditions. Thus, the fast and accurate extraction of yardangs is key to studying their regional dis...
Comprehensive and sustainable landslide risk management, including the identification of areas susceptible to landslides, requires responsible organisations to collaborate efficiently. Landslide risk management efforts are often made after major triggering events, such as hazard mitigation after the 2015 Gorkha earthquake in Nepal. There is also a...
Recurrent floods are one of the major global threats among people, particularly in developing countries like India, as this nation has a tropical monsoon type of climate. Therefore, flood susceptibility (FS) mapping is indeed necessary to overcome this type of natural hazard phenomena. With this in mind, we evaluated the prediction performance of F...
Construction of transportation infrastructure is a vital step in boosting economic and societal opportunities and often results in land use changes. In this study, we focus on the land use dynamics of the urban agglomeration around Hangzhou Bay, where the Qiantang River flows into the East China Sea. The Hangzhou Bay Bridge crosses the bay since 20...
Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnatak...
There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS...
The importance of freshwater for human societies and sustainable urban development is paramount. This study presents a new approach and framework for spatial modelling of urban drinking water consumption patterns (UDWCP) in light of a drinking water sustainability assessment. The approach was developed based on the GIS multi-criteria decision analy...
Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challengesnof urban complex systems requires advanced interdisciplinary analysis...
Landform mapping has increasingly become part of the digital domain. While the majority of approaches evaluates Digital Elevation Models (DEM) on a per-pixel basis, some examples exist were object-based image analysis (OBIA) has been applied to terrain data to identify a variety of landforms, including glacial landforms. The main objective of this...
Beyond the direct hazards of earthquakes, the deposited mass of earthquake-induced landslide (EQIL) in the riverbeds causing the river to thrust upward. The EQIL inventories are generated mostly by traditional or semi-supervised mapping approaches which required parameter's tuning or binary threshold decision in practical application. In this study...
Gully formation through water-induced soil erosion and related to devastating land degradation is often a quasi-normal threat to human life, as it is responsible for huge loss of surface soil. Therefore, gully erosion susceptibility (GES) mapping is necessary in order to reduce the adverse effect of land degradation and diminishes this type of harm...
The Rarh Bengal region in West Bengal, particularly the eastern fringe area of the Chotanagpur plateau, is highly prone to water-induced gully erosion. In this study, we analyzed the spatial patterns of a potential gully erosion in the Gandheswari watershed. This area is highly affected by monsoon rainfall and ongoing land-use changes. This combina...
The present research examines the landslide susceptibility in Rudraprayag district of Uttarakhand, India using the conditional probability (CP) statistical technique, the boost regression tree (BRT) machine learning algorithm, and the CP-BRT ensemble approach to improve the accuracy of the BRT model. Using the four fold of data, the models' outcome...
The uncertainty of flash flood makes them highly difficult to predict through conventional models. The physical hydrologic models of flash flood prediction of any large area is very difficult to compute as it requires lot of data and time. Therefore remote sensing data based models (from statistical to machine learning) have become highly popular d...
In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modeling and mapping considering eleven conditioning factors of soil typ...
Land surface temperature (LST), as an effective indicator measuring urban thermal environment, is significantly influenced by a range of human and natural factors at different scales. However, the scale-dependence of LST influencing factors has not been fully explored, due to relatively discrete scales or single factor used in previous studies. It...
ContextLocated between urban area and rural area, urban–rural fringe is challenged with urbanization related social-ecological problems. Accurately identifying the urban–rural fringe can help to integrated urban–rural development planning, especially in metropolitan region. Among the various case studies to identify the urban–rural fringe, land use...
Forest fires are considered one of the most highly damaging and devastating of natural disasters, causing considerable casualties and financial losses every year. Hence, it is important to produce susceptibility maps for the management of forest fires so as to reduce their harmful effects. The purpose of this study is to map the susceptibility to f...