Alfred Stein

• Professor
• Research Director at University of Twente

Image segmentation represents a fundamental step in analyzing very high spatial-resolution (VHR) remote sensing imagery. Its objective is to partition an image into segments that best match with geo-objects. However, the diverse appearances of geospatial objects often lead to inter-object homogeneity and intra-object heterogeneity. Existing segment...

Mosquito-borne diseases pose a significant public health concern in Colombia, necessitating robust quantification of their geographic patterns to guide and optimize interventions. This study explores the spatial dynamics and interactions among Zika, Dengue, and Chikungunya within the context of joint disease modeling in the Andean region of Colombi...

Accurate global glacier mapping is critical for understanding climate change impacts. Despite its importance, automated glacier mapping at a global scale remains largely unexplored. Here we address this gap and propose Glacier-VisionTransformer-U-Net (GlaViTU), a convolutional-transformer deep learning model, and five strategies for multitemporal g...

Precise and timely information about crop types plays a crucial role in various agriculture-related applications. However, crop type mapping methods often face significant challenges in cross-regional and cross-time scenarios with high discrepancies between temporal-spectral characteristics of crops from different regions and years. Unsupervised do...

Mining has played an important role in the economies of South American countries. Although industrial mining prevails in most countries, the expansion of garimpo activity has increased substantially. Recently, Brazil exhibited two moments of garimpo dominance over industrial mining: 1989–1997 and 2019–2022. While industrial mining sites occupied ~...

Machine learning (ML) models trained with remote sensing data have the potential to improve cereal yield estimation across various geographic scales. However, the complexity and heterogeneity of agricultural landscapes present significant challenges to the robustness of ML-based field-level yield estimation over large areas. In our study, we propos...

Low‐ and middle‐income countries shoulder the greatest burden of stunting and anaemia in children. This calls for prompt and effective intervention measures, while the contributing factors are not fully understood. This study evaluates determinants spanning from individual‐, household‐ and community levels including agroecology and antinutrients as...

Multivariate disease mapping is important for public health research, as it provides insights into spatial patterns of health outcomes. Geostatistical methods that are widely used for mapping spatially correlated health data encounter challenges when dealing with spatial count data. These include heterogeneity, zero-inflated distributions and unrel...

To realize the first sustainable development goal of ending “poverty in all its forms everywhere,” local governments in South Africa need to implement informed targeted policy interventions based on up‐to‐date data and sound analytics. Statistics South Africa (Stats SA) Censuses reveal the socioeconomic circumstances of people living in South Afric...

Mining has played a significant role in the economy of South American countries for centuries. Although industrial mining has become predominant in most countries, the expansion of the garimpos, here referred to as mechanized mining, has increased substantially. This type of mining, mainly related to gold, is not only harmful to human health due to...

Current semantic change detection (SCD) methods face challenges in modeling temporal correlations (TCs) between bitemporal semantic features and difference features. These methods lead to inaccurate detection results, particularly for complex SCD scenarios. This paper presents a hierarchical semantic graph interaction network (HGINet) for SCD from...

Integrated crop–livestock systems (ICLS) are among the main viable strategies for sustainable agricultural production. Mapping these systems is crucial for monitoring land use changes in Brazil, playing a significant role in promoting sustainable agricultural production. Due to the highly dynamic nature of ICLS management, mapping them is a challen...

The use of street view imagery (SVI) and advanced urban visual intelligence technologies has revolutionized built environment auditing (BEA) practice, by enabling high-resolution BEA at large scales. This study reviewed 96 studies of BEA published before October 2023. The Google SVI was employed in 92.7% of the included studies. Manual processing o...

Recently, deep learning methods have achieved promising crop mapping results. Yet, their classification performance is constrained by the scarcity of labeled samples. Therefore, the development of methods capable of exploiting label-rich environments to classify crops in label-scarce environments using only a few labeled samples per class is requir...

Extracting training datasets for supervised classification of Synthetic Aperture Radar (SAR) images is complicated, due to e.g. poor radiometric resolution, speckle noise, and lack of reference data. It is challenging to link radar scatterers in SAR images with the counterparts in the reference datasets registered in geographic coordinate systems....

Clouds in remote sensing optical images often obscure essential information. They may lead to occlusion or distortion of ground features, thereby affecting the subsequent analysis and extraction of target information. Therefore, the removal of clouds in optical images is a critical task in various applications. SAR-optical image fusion has achieved...

Accurately predicting the geometric structure of a building's roof as a vectorized representation from a raster image is a challenging task in building reconstruction. In this paper, we propose an efficient and precise parsing method called Roof-Former, based on a vision Transformer. Our method involves three steps: (1) Image encoder and edge node...

Co-circulation of diseases is a public health concern phenomenon as it often informs of population cross-exposure, susceptibility, and cross-protection dynamics. While it commonly occurs, spatial analysis predominately focuses on understanding the individual character of the involved diseases, often neglecting any contributions of co-circulating co...

This study focuses on the development of a new framework for evaluating bikeability in urban environments with the aim of enhancing sustainable urban transportation planning. To close the research gap that previous studies have disregarded the dynamic environmental factors and trajectory data, we propose a framework that comprises four sub-indices:...

Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are not available or present insignificant variations, which causes a common challenge for earth system science. Meanwhile, there are few spatial causation...

Poor‐quality diets are of huge concern in areas where consumption is dominated by locally sourced foods that provide inadequate nutrients. In agroecologically diverse countries like Ethiopia, food production is also likely to vary spatially. Yet, little is known about how nutrient production varies by agroecology. Our study looked at the adequacy o...

Measures of spatial association are important to reveal the spatial structures and patterns in geographical phenomena. They have utility for spatial interpolation, stochastic simulation, and causal inference, among others. Such measures are abundantly available for continuous spatial variables, whereas for categorical spatial variables they are les...

Image segmentation is a fundamental step in object-based image analysis and other workflows. However, high-efficiency remains a challenge, especially for the analysis of large-scale Earth observation images. In recent years, considerable effort has been paid to designing merging criteria, automatic scale selection, and object-specific optimisation....

Accurate segmentation of large areas from very high spatial-resolution (VHR) remote sensing imagery remains a challenging issue in image analysis. Existing supervised and unsupervised methods both suffer from the large variance of object sizes and the difficulty in scale selection, which often result in poor segmentation accuracies. To address the...

This paper presents a semantic edge-aware multi-task neural network (SEANet) to obtain closed boundaries when delineating agricultural parcels from remote sensing images. It derives closed boundaries from remote sensing images and improves conventional semantic segmentation methods for the extraction of small and irregular agricultural parcels. SEA...

Traditional acquisition of height data to generate normalized digital surface models (nDSMs) of very high spatial resolution is time-consuming and expensive. Height estimation by means of optical remote sensing images is a more efficient and timely way to do so. Recent studies employed supervised learning methods. State-of-the-art computer vision m...

Background Epidemiological studies have widely proven the impact of ozone (O3) on respiratory mortality, while only a few studies compared the association between different O3 indicators and health. Methods This study explores the relationship between daily respiratory hospitalization and multiple ozone indicators in Guangzhou, China, from 2014 to...

Increased ozone pollution has greatly negative impact on human health. This study aimed to update evidence on the effects of short-term ozone exposure on three major health endpoints in China: all-cause, cardiovascular and respiratory mortality. The specific epidemiological study in a single city or a typical region cannot fully describe the health...

This paper presents a new multi-task neural network, called BsiNet, to delineate agricultural fields from high-resolution satellite images. BsiNet is modified from a Psi-Net by structuring three parallel decoders into a single encoder to improve computational efficiency. BsiNet learns three tasks: a core task for agricultural field identification a...

The analysis of point patterns on linear networks is receiving current attention in spatial statistics. This refers to the analysis of points in a spatial domain that coincide with a linear network like a road network. The linear network is modelled as a set of lines that are connected at their ends or are intersecting, that is, modelled as mathema...

The Groningen gas field, the largest natural gas field in Europe, was discovered in 1959 and started its production in 1963. The Earth surface above it experienced subsidence over the past six decades because of gas extraction activities. To accurately reveal this surface movement with satellite SAR data, our study first proposes and demonstrates a...

This paper presents a meta-analysis of the impacts of short-term exposure to ozone (O3) on three health endpoints: all-cause, cardiovascular, and respiratory mortality in China. All relevant studies from January 1990 to December 2021 were searched from four databases. After screening, 30 studies were included for the meta-analysis. The results show...

Sinkholes exhibit precursory deformation patterns. Such deformation patterns can be studied using InSAR time-series analysis over constantly coherent scatterrers (CCS). In the past we identified Heaviside and Breakpoint changes as two important forms of anomalous behavior. It is challenging to efficiently detect and classify these sudden step and s...

Recently, we have shown that sinkholes can be characterized at an early stage by precursory deformation patterns from InSAR time series [1]. These patterns are often related to sudden changes in deformations or deformation velocities. With such a priori information, accurate deformation modelling and early detection of precursory patterns is feasib...

Roof structure information is essential for creating detailed 3D building models. These serve in many applications that require knowledge of the roof type and geometry. Automated extraction of the roof structure from remotely sensed images is a challenge because of scene complexity and the large variety of roof top configurations. This paper introd...

Satellite image data deliver consistent and frequent information for crop yield estimation over large areas. Hyperspectral narrowbands are more sensitive spectrally to changes in crop growth than multispectral broadbands but few studies quantified the gains in the former over the later. The PRecursore IperSpettrale della Missione Applicativa (PRISM...

Heavy metal soil pollution is a worldwide problem. It is affected by many natural and human factors through heterogeneous relationships. Accurate prediction at unobserved locations using a limited number of observations hence remains a challenge. This study proposes a two-point machine learning method to fully utilize the information in spatial nei...

Information extraction is a key activity for remote sensing images. A common distinction exists between knowledge-driven and data-driven methods. Knowledge-driven methods have advanced reasoning ability and interpretability, but have difficulty in handling complicated tasks since prior knowledge is usually limited when facing the highly complex spa...

This paper summarizes the development and application of spatial statistical models in satellite optical remote sensing. The paper focuses on the development of a conceptual model that includes the measurement and sampling processes inherent in remote sensing. We organized this paper into five main sections: introducing the basis of remote sensing,...

The spatial distribution of nuraghes throughout the Island of Sardinia still raises many questions. In this paper, we apply spatial statistical methods to investigate their relations with topographical features and with related objects nearby. We use the non-stationary G- and J-functions. To model interactions with topographic variables we use the...

Poverty affects many people worldwide and varies in space and time, although its determinants are geographical factors. This paper presents a case study from Hubei Province, Central China, investigating the spatial and temporal changes in poverty determinants at the county and village levels from 2013 to 2017. We investigated the variation in the s...

Finding cause–effect relationships behind observed phenomena remains a challenge in spatial analysis. In recent years, much progress in causal inference has been made in statistics, economics, epidemiology and computer sciences, but limited progress has been made in spatial statistics due to the nonrandom, nonrepeatability and synchronism of spatia...

Background Vancomycin-resistant enterococci (VRE) is the cause of severe patient health and monetary burdens. Antibiotic use is a confounding effect to predict VRE in patients, but the antibiotic use of patients who may have frequented the same ward as the patient in question is often neglected. This study investigates how patient movements between...

Sea ice plays a significant role in global climate change. Marginal ice zone (MIZ) is defined as the transition zone between open ocean and pack ice where intensive air-ice-ocean-wave interactions between the ocean and the atmosphere occur. This definition of MIZ is rather vague, which affects its mapping. Previous data-driven methods extracted MIZ...

Apartheid laws resulted in racial residential segregation that became entrenched into the urban morphology of South Africa. When apar-theid ended in the 1990’s, the new South African democratic government was resolved to bring about social and spatial justice, address inequalities and promote social cohesion. To determine progress towards racial re...

Objective Antimicrobial resistance (AMR) is a global threat to health and healthcare. In response to the growing AMR burden, research funding also increased. However, a comprehensive overview of the research output, including conceptual, temporal, and geographical trends, is missing. Therefore, this study uses topic modelling, a machine learning ap...

The marginal ice zone (MIZ) around Antarctica is an important air–ice–ocean–wave interaction area and a crucial habitat for marine life. Given its dynamic nature, it is essential to understand and quantify both the short- and long-term changes of its extent. In this study, we investigated the MIZ extent time series using multifractal spectrum and [...

Remote Sensing (RS) offers efficient tools for drought monitoring, especially in countries with a lack of reliable and consistent in-situ multi-temporal datasets. In this study, a novel RS-based Drought Index (RSDI) named Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI) was proposed. To the best of our knowledge, TVMPDI is...

Background: Vancomycin-resistant enterococci (VRE) is the cause of severe patient health and monetary burdens. Antibiotic use is a confounding effect to predict VRE in patients, but the antibiotic use of patients who may have frequented the same ward as the patient in question is often neglected. This study investigated how the occurrence and sprea...

Wildfire continues to be a major environmental problem in the world. To help land and fire management agencies manage and mitigate wildfire-related risks, we need to develop tools for mapping those risks. Big geodata—in the form of remotely sensed images, ground-based sensor observations, and topographical datasets—can help us characterize the dyna...

Sinkholes are sudden disasters that are usually small in size and occur at unexpected locations. They may cause serious damage to life and property. Sinkhole-prone areas can be monitored using Interferometric Synthetic Aperture Radar (InSAR) time series. Defining a pattern using InSAR-derived spatio-temporal deformations, this study presents a sink...

GPS sensors have an inherent positional uncertainty that is often neglected in environmental modeling. In this article we study the propagation of positional uncertainty in grid-based geo-information systems. The probability is obtained that a point has an actual position outside the raster cell in which it was observed. For multiple points, these...

A recent development in Interferometric Synthetic Aperture Radar (InSAR) technology is integrating multiple SAR satellite data to dynamically extract ground features. This paper addresses two relevant challenges: identification of common ground targets from different SAR datasets in space, and concatenation of time series when dealing with temporal...

Background: Schools, depending on their access to and quality of water, sanitation and hygiene (WASH) and the implementation of healthy behaviours, can be critical for the control and spread of many infectious diseases, including COVID-19. Schools provide opportunities for pupils to learn about the importance of hygiene and WASH-related practice, a...

This paper focuses on spatial quality assessment of pan-sharpened imagery that contains valuable information of input images. Its aim is to show that fusion functions respond differently to different types of landscapes. It compares a quality assessment of an object-level procedure with that of a conventional pixel-level-based procedure which assig...

In this contribution, we investigate PAZ co-polarimetric SAR data applicability for surface movement mapping and scattering characterization. PAZ simultaneously collects SAR imagery in both VV and HH channels. Using a small stack of PAZ data, we apply the real-valued impulse response function correlation to identify constantly coherent scatterers (...

Background Hand transmission of harmful microorganisms may lead to infections and poses a major threat to patients and healthcare workers in healthcare settings. The most effective countermeasure against these transmissions is the adherence to spatiotemporal hand hygiene policies, but adherence rates are relatively low and vary over space and time....

The quality of volunteered geographic information (VGI) is questionable as it emerges through a diversity of contributors. The reputation of a contributor is increasingly applied to VGI quality assessment and its assurance. Research on how to measure and validate reputation, however, is still required. This study proposes an evaluation‐based weight...

Information on cultivated crops is relevant for a large number of food security studies. Different scientific efforts are dedicated to generate this information from remote sensing images by means of machine learning methods. Unfortunately, these methods do not account for the spatial-temporal relationships inherent in remote sensing images. In our...

Pan-sharpening methods are commonly used to synthesize multispectral and panchromatic images. Selecting an appropriate algorithm that maintains the spectral and spatial information content of input images is a challenging task. This review paper investigates a wide range of algorithms, including 41 methods. For this purpose, the methods were catego...

Spatial information regarding the arrangement of land cover objects plays an important role in distinguishing the land use types at land parcel or local neighborhood levels. This study investigates the use of graph convolutional networks (GCNs) in order to characterize spatial arrangement features for land use classification from high resolution re...

This research investigates the use of scale-space theory to detect individual trees in orchards from very-high resolution (VHR) satellite images. Trees are characterized by blobs, for example, bell-shaped surfaces. Their modeling requires the identification of local maxima in Gaussian scale space, whereas location of the maxima in the scale directi...

Efforts are being made to contain rabies in Tanzania, reported in the southern highland regions, since 1954, and endemic in all districts in Tanzania currently. It has been determined that mass vaccination of at least 70% of a domestic animal population is most effective in reducing transmission of rabies. Current vaccination campaigns in Tanzanian...

Geospatial referenced environmental data are extensively used in environmental assessment, prediction, and management. Data are commonly obtained by nonrandom surveys or monitoring networks, whereas spatial sampling and inference affect the accuracy of subsequent applications. Design-based and model-based procedures (DB and MB for short) both allow...

De Nederlandse Aardolie Maatschappij BV (NAM) heeft van het Rijk toestemming om aardgas te winnen uit zes velden in het Waddenzeegebied: Moddergat, Nes, Lauwersoog C, Lauwersoog West, Lauwersoog Oost en Vierhuizen Oost (verder MLV-gasvelden). De winning is gestart in 2007. De belangrijkste voorwaarde is dat de bodem door de gaswinning niet meer daa...

Crop type mapping is relevant to a wide range of food security applications. Supervised classification methods commonly generate these data from satellite image time-series. Yet, their successful implementation is hindered by the lack of training samples. Solutions like transfer learning, development of temporal-spectral signatures of the target cl...

Objective The objective of this study was to identify risk factors for surgical site infection from digestive, thoracic and orthopaedic system surgeries using clinical and data-driven cut-off values. A second objective was to compare the identified risk factors in this study to risk factors identified in literature. Summary background data Retrosp...

The Moon has a large potential for space exploration and mining valuable resources. In particular, 3He provides rich sources of non-radioactive fusion fuel to fulfill cislunar and Earth's energy demands, if found economically feasible. The present study focuses on developing advanced techniques to prospect 3He resources on the Moon from multi-senso...

The composition and arrangement of spatial entities, i.e., land cover objects, play a key role in distinguishing land use types from very high resolution (VHR) remote sensing images, in particular in urban environments. This paper presents a new method to characterize the spatial arrangement for urban land use extraction using VHR images. We derive...

Air quality is a common cause for respiratory health problems. It shows high temporal and spatial variability within urban areas and currently sensors are installed to monitor air quality. The objective of this paper is to investigate its spatial variability during peak hours. Spatial statistical methods are based upon copula theory, integrating di...

This is the editorial letter for the Special Issue dedicated to the conference Spatial Statistics 2019 Towards Spatial Data Science held in Sitges (Spain) from July 10 to 13, 2019. This fifth international conference on Spatial Statistics was run under the theme Towards Spatial Data Science with the aim to honour the emerging field of Data Science...

Monitoring of variables like temperature, precipitation, and air quality is performed to determine their current situation, exhibit the presence of trends and occurrence of outliers. These variables are measured at specific locations and to obtain a full estimation map, we need to predict values at unknown locations. This study focuses on making a...

Background Transmission of harmful microorganisms may lead to infections and poses a major threat to patients and healthcare workers in healthcare settings. The most effective countermeasure against the transmission and spread of harmful microorganisms is the adherence to spatiotemporal hand hygiene policies, but adherence rates are relatively low...

Background: Transmission of harmful microorganisms may lead to infections and poses a major threat to patients and healthcare workers in healthcare settings. The most effective countermeasure against the transmission and spread of harmful microorganisms is the adherence to spatiotemporal hand hygiene policies, but adherence rates are relatively low...