Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges

In recent decades, we have witnessed great advances on the Internet of Things, mobile devices, sensor-based systems, and resulting big data infrastructures, which have gradually, yet fundamentally influenced the way people interact with and in the digital and physical world. Many human activities now not only operate in geographical (physical) space but also in cyberspace. Such changes have triggered a paradigm shift in geographic information science (GIScience), as cyberspace brings new perspectives for the roles played by spatial and temporal dimensions, e.g., the dilemma of placelessness and possible timelessness. As a discipline at the brink of even bigger changes made possible by machine learning and artificial intelligence, this paper highlights the challenges and opportunities associated with geographical space in relation to cyberspace, with a particular focus on data analytics and visualization, including extended AI capabilities and virtual reality representations. Consequently, we encourage the creation of synergies between the processing and analysis of geographical and cyber data to improve sustainability and solve complex problems with geospatial applications and other digital advancements in urban and environmental sciences

Integrating population dynamics into mapping human exposure to seismic hazard

Disaster risk is not fully characterized without taking into account vulnerability and population exposure. Assessment of earthquake risk in urban areas would benefit from considering the variation of population distribution at more detailed spatial and temporal scales, and from a more explicit integration of this improved demographic data with existing seismic hazard maps. In the present work, "intelligent" dasymetric mapping is used to model population dynamics at high spatial resolution in order to benefit the analysis of spatio-temporal exposure to earthquake hazard in a metropolitan area. These night- and daytime-specific population densities are then classified and combined with seismic intensity levels to derive new spatially-explicit four-class-composite maps of human exposure. The presented approach enables a more thorough assessment of population exposure to earthquake hazard. Results show that there are significantly more people potentially at risk in the daytime period, demonstrating the shifting nature of population exposure in the daily cycle and the need to move beyond conventional residence-based demographic data sources to improve risk analyses. The proposed fine-scale maps of human exposure to seismic intensity are mainly aimed at benefiting visualization and communication of earthquake risk, but can be valuable in all phases of the disaster management process where knowledge of population densities is relevant for decision-making

Towards quantifying the effects of resource extraction on land cover and topography through remote sensing analysis: Confronting issues of scale and data scarcity

This dissertation focuses on the mapping and monitoring of mineral mining activity using remotely sensed data. More specifically, it explores the challenges and issues associated with remote sensing-based analysis of land use land cover (LULC) and topographic changes in the landscape associated with artisanal and industrial-scale mining. It explores broad themes of image analysis, including evaluation of error in digital elevation models (DEMs), integration of multiple scales and data sources, quantification of change, and remote sensing classification in data-scarce environments. The dissertation comprises three case studies.;The first case study examines the LULC change associated with two scales of mining activity (industrial and artisanal) near Tortiya, Cote d\u27Ivoire. Industrial mining activity was successfully mapped in a regional LULC classification using Landsat multispectral imagery and support vector machines (SVMs). However, mapping artisanal mining required high-resolution imagery to discriminate the small, complex patterns of associated disturbance.;The second case study is an investigation of the potential for quantifying topographic change associated with mountain top removal mining and the associated valley-fill operations for a region in West Virginia, USA, using publicly available DEMs. A 1:24,000 topographic map data, the shuttle radar topography mission (SRTM) DEM, a state-wide photogrammetric DEM, and the Advanced Spaceborne Thermal Emission Radiometer (ASTER) Global DEM (GDEM) were compared to a lidar bare-earth reference DEM. The observed mean error in both the SRTM and GDEM was statistically different than zero and modeled a surface well above the reference DEM surface. Mean error in the other DEMs was lower, and not significantly different than zero. The magnitude of the root mean square error (RMSE) suggests that only topographic change associated with the largest topographic disturbances would be separable from background noise using global DEMS such as the SRTM. Nevertheless, regionally available DEMs from photogrammetric sources allow mapping of mining change and quantification of the total volume of earth removal.;Monitoring topographic change associated with mining is challenging in regions where publicly available DEMs are limited or not available. This challenge is particularly acute for artisanal mining, where the topographic disturbance, though locally important, is unlikely to be detected in global elevation data sets. Therefore, the third and final case study explored the potential for creating fine-spatial resolution bare-earth DEMs from digital surface models (DSMs) using high spatial resolution commercial satellite imagery and subsequent filtering of elevation artifacts using commercial lidar software and other spatial filtering techniques. Leaf-on and leaf-off DSMs were compared to highlight the effect of vegetation on derived bare-earth DEM accuracy. The raw leaf-off DSM was found to have very low error overall, with notably higher error in areas of evergreen vegetation. The raw leaf-on DSM was found to have a RMSE error much higher than the leaf-off data, and similar to that of the SRTM in dense deciduous forest. However, filtering using the commercial techniques developed for lidar notably reduced the error present in the raw DSMs, suggesting that such approaches could help overcome data scarcity in regions where regional or national elevation data sets are not available.;Collectively this research addressed data issues and methodological challenges in the analysis of 3D changes caused by resource extraction. Elevation and optical imagery are key data sets for mapping the disturbance associated with mining. The particular combination required regarding data spatial scale, and for elevation, accuracy, is a function of the type and scale of the mining

Multi-task deep learning for large-scale building detail extraction from high-resolution satellite imagery

Understanding urban dynamics and promoting sustainable development requires comprehensive insights about buildings. While geospatial artificial intelligence has advanced the extraction of such details from Earth observational data, existing methods often suffer from computational inefficiencies and inconsistencies when compiling unified building-related datasets for practical applications. To bridge this gap, we introduce the Multi-task Building Refiner (MT-BR), an adaptable neural network tailored for simultaneous extraction of spatial and attributional building details from high-resolution satellite imagery, exemplified by building rooftops, urban functional types, and roof architectural types. Notably, MT-BR can be fine-tuned to incorporate additional building details, extending its applicability. For large-scale applications, we devise a novel spatial sampling scheme that strategically selects limited but representative image samples. This process optimizes both the spatial distribution of samples and the urban environmental characteristics they contain, thus enhancing extraction effectiveness while curtailing data preparation expenditures. We further enhance MT-BR's predictive performance and generalization capabilities through the integration of advanced augmentation techniques. Our quantitative results highlight the efficacy of the proposed methods. Specifically, networks trained with datasets curated via our sampling method demonstrate improved predictive accuracy relative to those using alternative sampling approaches, with no alterations to network architecture. Moreover, MT-BR consistently outperforms other state-of-the-art methods in extracting building details across various metrics. The real-world practicality is also demonstrated in an application across Shanghai, generating a unified dataset that encompasses both the spatial and attributional details of buildings

Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems

The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions to contemporary foundational GIS technology raises fundamental questions concerning the ontological, formal representational, and (analytical) computational methods that would underlie their spatial information theoretic underpinnings. We present the conceptual overview and architecture for the development of high-level semantic and qualitative analytical capabilities for dynamic geospatial domains. Building on formal methods in the areas of commonsense reasoning, qualitative reasoning, spatial and temporal representation and reasoning, reasoning about actions and change, and computational models of narrative, we identify concrete theoretical and practical challenges that accrue in the context of formal reasoning about `space, events, actions, and change'. With this as a basis, and within the backdrop of an illustrated scenario involving the spatio-temporal dynamics of urban narratives, we address specific problems and solutions techniques chiefly involving `qualitative abstraction', `data integration and spatial consistency', and `practical geospatial abduction'. From a broad topical viewpoint, we propose that next-generation dynamic GIS technology demands a transdisciplinary scientific perspective that brings together Geography, Artificial Intelligence, and Cognitive Science. Keywords: artificial intelligence; cognitive systems; human-computer interaction; geographic information systems; spatio-temporal dynamics; computational models of narrative; geospatial analysis; geospatial modelling; ontology; qualitative spatial modelling and reasoning; spatial assistance systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964); Special Issue on: Geospatial Monitoring and Modelling of Environmental Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press

Remote Sensing for Monitoring the Mountaintop Mining Landscape: Applications for Land Cover Mapping at the Individual Mine Complex Scale

The aim of this dissertation was to investigate the potential for mapping land cover associated with mountaintop mining in Southern West Virginia using high spatial resolution aerial- and satellite-based multispectral imagery, as well as light detection and ranging (LiDAR) elevation data and terrain derivatives. The following research themes were explored: comparing aerial- and satellite-based imagery, combining data sets of multiple dates and types, incorporating measures of texture, using nonparametric, machine learning classification algorithms, and employing a geographical object-based image analysis (GEOBIA) framework. This research is presented as four interrelated manuscripts.;In a comparison of aerial National Agriculture Imagery Program (NAIP) orthophotography and satellite-based RapidEye data, the aerial imagery was found to provide statistically less accurate classifications of land cover. These lower accuracies are most likely due to inconsistent viewing geometry and radiometric normalization associated with the aerial imagery. Nevertheless, NAIP orthophotography has many characteristics that make it useful for surface mine mapping and monitoring, including its availability for multiple years, a general lack of cloud cover, contiguous coverage of large areas, ease of availability, and low cost. The lower accuracies of the NAIP classifications were somewhat remediated by decreasing the spatial resolution and reducing the number of classes mapped.;Combining LiDAR with multispectral imagery statistically improved the classification of mining and mine reclamation land cover in comparison to only using multispectral data for both pixel-based and GEOBIA classification. This suggests that the reduced spectral resolution of high spatial resolution data can be combated by incorporating data from another sensor.;Generally, the support vector machines (SVM) algorithm provided higher classification accuracies in comparison to random forests (RF) and boosted classification and regression trees (CART) for both pixel-based and GEOBIA classification. It also outperformed k-nearest neighbor, the algorithm commonly used for GEOBIA classification. However, optimizing user-defined parameters for the SVM algorithm tends to be more complex in comparison to the other algorithms. In particular, RF has fewer parameters, and the program seems robust regarding the parameter settings. RF also offers measures to assess model performance, such as estimates of variable importance and overall accuracy.;Textural measures were found to be of marginal value for pixel-based classification. For GEOBIA, neither measures of texture nor object-specific geometry improved the classification accuracy. Notably, the incorporation of additional information from LiDAR provided a greater improvement in classification accuracy then deriving complex textural and geometric measures.;Pre- and post-mining terrain data classified using GEOBIA and machine learning algorithms resulted in significantly more accurate differentiation of mine-reclaimed and non-mining grasslands than was possible with spectral data. The combination of pre- and post-mining terrain data or just pre-mining data generally outperformed post-mining data. Elevation change data were shown to be of particular value, as were terrain shape parameters. GEOBIA was a valuable tool for combining data collected using different sensors and gridded at variable cell sizes, and machine learning algorithms were particularly useful for incorporating the ancillary data derived from the digital elevation models (DEMs), since these most likely would not have met the basic assumptions of multivariate normality required for parametric classifiers.;Collectively, this research suggests that high spatial resolution remotely sensed data are valuable for mapping and monitoring surface mining and mine reclamation, especially when elevation and spectral data are combined. Machine learning algorithms and GEOBIA are useful for integrating such diverse data

Integrating openstreetmap data and sentinel-2 Imagery for classifying and monitoring informal settlements

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe identification and monitoring of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical to efforts to improve their resilience. This study aims at integrating OSM data and sentinel-2 imagery for classifying and monitoring the growth of informal settlements methods to map informal areas in Kampala (Uganda) and Dar es Salaam (Tanzania) and to monitor their growth in Kampala. Three building feature characteristics of size, shape and Distance to nearest Neighbour were derived and used to cluster and classify informal areas using Hotspot Cluster analysis and ML approach on OSM buildings data. The resultant informal regions in Kampala were used with Sentinel-2 image tiles to investigate the spatiotemporal changes in informal areas using Convolutional Neural Networks (CNNs). Results from Optimized Hot Spot Analysis and Random Forest Classification show that Informal regions can be mapped based on building outline characteristics. An accuracy of 90.3% was achieved when an optimally trained CNN was executed on a test set of 2019 satellite image tiles. Predictions of informality from new datasets for the years 2016 and 2017 provided promising results on combining different open source geospatial datasets to identify, classify and monitor informal settlements

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

Extracting Computational Representations of Place with Social Sensing

Place-based GIS are at the forefront of GIScience research and characterized by textual descriptions, human conceptualizations as well as the spatial-semantic relationships among places. The concepts of places are difficult to handle in geographic information science and systems because of their intrinsic vagueness. They arise from the complex interaction of individuals, society, and the environment. The exact delineation of vague regions is challenging as their borders are vague and the membership within a region varies non-monotonically and as a function of context. Consequently, vague regions are difficult to handle computationally, e.g., in spatial analysis, cartography, geographic information retrieval, and GIS workflows in general. The emergence of big data brings new opportunities for us to understand the place semantics from large-scale volunteered geographic information and data streams, such as geotags, texts, activity streams, and GPS trajectories. The term "social sensing" describes such individual-level big geospatial data and the associated analysis methods. In this dissertation, I present a generalizable, data-driven framework that complements classical top-down approaches by extracting the representations of vague cognitive regions and function regions from bottom-up approaches using spatial statistics and machine learning techniques with various social sensing sources. I demonstrate how to derive crisp boundaries for cognitive and functional regions from points of interest data, and show how natural language processing techniques can enrich our understanding of places and form a foundation for the semantic characterization of place types and the generalization of regions. This work makes contributions to the development of computational methodologies for extracting vague cognitive regions and functional regions using data-driven approaches as well as the novel semantic generalization processing technique

Uso y cobertura del suelo en las islas macaronésicas de Portugal y España: nuevos métodos para cuantificar y visualizar información de patrones espaciales

Tesis de la Universidad Complutense de Madrid, Facultad de Geografía e Historia, Departamento de Geografía Humana, leída el 23/11/2016The aim of this research is to propose novel methods for quantifying and visualizing geographical information, in order to aid the spatial planning decision-making process when addressing land use and land cover patterns. In doing so, several modeling and geographic visualization methods are developed and demonstrated by using the Macaronesian islands of Portugal and Spain as study areas. Macaronesia is a biogeographical region consisting of several archipelagos in the Atlantic Ocean belonging to three countries: Portugal, Spain, and Cape Verde. This research encompasses three archipelagos: the Azores, Madeira, and the Canary Islands. From these three archipelagos, the four most densely populated islands were further selected for the land use and land cover assessments: São Miguel, Madeira, Tenerife, and Gran Canaria. A common feature of the Macaronesian islands is that, ever since European colonization in the fifteenth century, up until the mid-twentieth century, anthropogenic land change was predominately attributable to agricultural activities consuming forests and natural areas. In the mid-twentieth century, owing to profound social and economic changes, the tertiary sector started its rise in becoming the main economic sector. Because the secondary sector in this region has always been minor, this substantial shift to the tertiary sector would dictate a progressive abandonment of the primary sector. Hence, agricultural areas started to recede. As a result, the last decades of the twentieth century were marked by a significant shift in land use dynamics. Agricultural activities ceased to be the main driving force of land change and were replaced by a rampant increase of the artificial surfaces, mainly on the southern coastal areas, where tourism-related and real estate pressure constitute a major impact on the landscape. A direct consequence of this pressure was the drastic transformation across the islands’ leeward coastal landscapes...El objetivo principal de esta investigación es proponer nuevos métodos para cuantificar y visualizar información geográfica, con el fin de facilitar el proceso de toma de decisiones en relación a los patrones de uso y ocupación del suelo. De este modo, se desarrollan y aplican varios métodos de modelación y visualización geográfica, utilizando las islas macaronésicas de Portugal y España como áreas de estudio. La Macaronesia es una región biogeográfica que integra varios archipiélagos en el Océano Atlántico pertenecientes a tres países: Portugal, España y Cabo Verde. Esta investigación abarca tres archipiélagos: Azores, Madeira y Canarias. Para una evaluación detallada de uso y cobertura del suelo se seleccionaron las cuatro islas más densamente pobladas: San Miguel, Madeira, Tenerife y Gran Canaria. Una característica común a las islas macaronésicas es que, desde de la colonización en el siglo XV hasta mediados del siglo XX, el cambio antropogénico del suelo se debió principalmente a las actividades agrícolas, que ocuparon bosques y áreas naturales. A mediados del siglo XX, debido a profundos cambios sociales y económicos, el sector terciario empezó su ascenso para convertirse en el principal sector económico. Debido a que el sector secundario en esta región siempre ha tenido una importancia menor, este proceso de terciarización de la economía supuso un progresivo abandono del sector primario. Por lo tanto, las áreas agrícolas comenzaron a experimentar un claro retroceso. Como resultado de este proceso, las últimas décadas del siglo XX se caracterizaron por un cambio significativo en las dinámicas de uso y cobertura del suelo. Las actividades agrícolas dejaron de ser la principal fuerza impulsora en el cambio de lo suelo y fueron reemplazadas por el aumento desenfrenado de las superficies artificiales, principalmente en las zonas costeras del sur, donde el turismo y la especulación inmobiliaria ejercen una gran presión sobre el paisaje. Consecuencia directa de esta presión fueron las drásticas transformaciones de los paisajes costeros de las islas...Esta investigação tem como principal objectivo propor novos métodos para quantificar e visualizar informação geográfica, de modo a auxiliar o processo de tomada de decisão quando seja necessário analisar padrões de uso e ocupação do solo. Ao longo da investigação são apresentados vários métodos de modelação e visualização geográfica, usando como área de estudo as ilhas da Macaronésia pertencentes a Portugal e Espanha. A Macaronésia é uma região biogeográfica no Oceano Atlântico constituída por vários arquipélagos pertencentes a três países: Portugal, Espanha e Cabo Verde. Este trabalho de investigação abrange três arquipélagos: os Açores, a Madeira e as Ilhas Canárias. Para uma avaliação mais detalhada quanto ao uso e ocupação do solo, foram seleccionadas as quatro ilhas mais densamente povoadas: São Miguel, Madeira, Gran Canaria e Tenerife. Uma característica comum às ilhas da Macaronésia reside na particularidade de, desde a sua colonização no século XV, até meados do século XX, as alterações antropogénicas do solo terem estado predominantemente associadas às actividades agrícolas que consumiram extensas áreas de floresta e espaços naturais. Em meados do século XX, devido a profundas alterações sociais e económicas, o sector terciário iniciou a sua ascensão para se tornar o principal sector económico. Uma vez que, nesta região, o sector secundário foi sempre pouco significativo, a terciarização da actividade económica ditou um progressivo abandono do sector primário. Deste modo, as áreas agrícolas começaram a recuar. Como resultado deste processo, as últimas décadas do século XX foram marcadas por uma mudança significativa na dinâmica de uso e ocupação do solo nas ilhas desta região. As actividades agrícolas deixaram de ser a principal força motriz para as alterações no uso do solo, sendo substituídas pelo aumento galopante das superfícies artificiais, principalmente nas áreas costeiras do sul, onde as actividades relacionadas com o turismo e a especulação imobiliária causaram um grande impacto na paisagem, e contribuiram para a transformação drástica do litoral sotavento das ilhas...Depto. de GeografíaFac. de Geografía e HistoriaTRUEunpu