Semantics-enabled Spatio-Temporal Modeling of Earth Observation Data

Kurte, Kuldeep; Potnis, Abhishek V.; Durbha, Surya S.

doi:10.1145/3356395.3365545

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…Flood duration products have not been considered in the context of emergency mapping to date, due to a lack of automation of existing methods [20][21][22] and often limited data availability from the onset of a disaster. Existing studies have not specifically considered this aspect and focused only on using data from single sensors [22][23][24][25][26]29,30] or sensors groups of optical [27] or SAR [20,21] missions.…”

Section: Discussionmentioning

confidence: 99%

Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data

2020

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Flood duration is a crucial parameter for disaster impact assessment as it can directly influence the degree of economic losses and damage to structures. It also provides an indication of the spatio-temporal persistence and the evolution of inundation events. Thus, it helps gain a better understanding of hydrological conditions and surface water availability and provides valuable insights for land-use planning. The objective of this work is to develop an automatic procedure to estimate flood duration and the uncertainty associated with the use of multi-temporal flood extent masks upon which the procedure is based. To ensure sufficiently high observation frequencies, data from multiple satellites, namely Sentinel-1, Sentinel-2, Landsat-8 and TerraSAR-X, are analyzed. Satellite image processing and analysis is carried out in near real-time with an integrated system of dedicated processing chains for the delineation of flood extents from the range of aforementioned sensors. The skill of the proposed method to support satellite-based emergency mapping activities is demonstrated on two cases, namely the 2019 flood in Sofala, Mozambique and the 2017 flood in Bihar, India.

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Section: Discussionmentioning

confidence: 99%

Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data

2020

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“…The Dynamic Flood Ontology (DFO) [36] is an ontological upper model to represent the spatiotemporal changes occurring in a flood disaster situation. This can be used to make queries relevant during an urban flood scenario to the purpose of situational awareness.…”

Section: Ontologies For Crisis Managementmentioning

confidence: 99%

Smart City Ontologies and Their Applications: A Systematic Literature Review

Nicola

Villani

2021

Sustainability

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The increasing interconnections of city services, the explosion of available urban data, and the need for multidisciplinary analysis and decision making for city sustainability require new technological solutions to cope with such complexity. Ontologies have become viable and effective tools to practitioners for developing applications requiring data and process interoperability, big data management, and automated reasoning on knowledge. We investigate how and to what extent ontologies have been used to support smart city services and we provide a comprehensive reference on what problems have been addressed and what has been achieved so far with ontology-based applications. To this purpose, we conducted a systematic literature review finalized to presenting the ontologies, and the methods and technological systems where ontologies play a relevant role in shaping current smart cities. Based on the result of the review process, we also propose a classification of the sub-domains of the city addressed by the ontologies we found, and the research issues that have been considered so far by the scientific community. We highlight those for which semantic technologies have been mostly demonstrated to be effective to enhance the smart city concept and, finally, discuss in more details about some open problems.

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“…proposed an approach to obtain waterlogging depth from video images using CNN. Hong et al, 2004;Hayatbini et al, 2019;Pan et al, 2019;Potnis et al, 2019;Jain et al, 2020;Jiang et al, 2020 Knowledge-based approaches Kurte et al (2017) used a semantics-driven framework to enable spatial relationships based semantic queries to detect flooded regions from satellite imagery and further extended the framework (Kurte et al, 2019) to accommodate temporal dimension that enabled spatio-temporal queries over flooded regions. In a similar approach, Potnis et al (2018) developed a flood scene ontology (FSO) which formally defines complex classes such as Accessible Residential Buildings, to classify flooded regions in urban area from satellite imagery.…”

Section: Xu Et Al 2019amentioning

confidence: 99%

“…Kurte et al (2017) proposed a semantics enabled framework to model the spatial relationships among various regions in the RS images to enable spatial-relationships-based queries such as Retrieve all images in the ALI repository having Built Up region externally connected to the Stagnated Flood Water. Later this work was extended to accommodate the temporal aspect to enable the spatio-temporal semantic queries such as Show road segments which were completely submerged during 9th September 2014 to 22nd September 2014 (Kurte et al, 2019). In a similar semantics based approach, Potnis et al (2018) developed a flood scene ontology (FSO) which formally defines complex classes such as Flooded_Residential_Buildings, Accessible_Residential_Buildings, Operational_Roads.…”

Section: Flood Managementmentioning

confidence: 99%

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Allen‐Dumas

Kurte

et al. 2021

Front. Water

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Due to the complex interactions of human activity and the hydrological cycle, achieving urban water security requires comprehensive planning processes that address urban water hazards using a holistic approach. However, the effective implementation of such an approach requires the collection and curation of large amounts of disparate data, and reliable methods for modeling processes that may be co-evolutionary yet traditionally represented in non-integrable ways. In recent decades, many hydrological studies have utilized advanced machine learning and information technologies to approximate and predict physical processes, yet none have synthesized these methods into a comprehensive urban water security plan. In this paper, we review ways in which advanced machine learning techniques have been applied to specific aspects of the hydrological cycle and discuss their potential applications for addressing challenges in mitigating multiple water hazards over urban areas. We also describe a vision that integrates these machine learning applications into a comprehensive watershed-to-community planning workflow for smart-cities management of urban water resources.

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Semantics-enabled Spatio-Temporal Modeling of Earth Observation Data

Cited by 10 publications

References 14 publications

Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data

Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data

Smart City Ontologies and Their Applications: A Systematic Literature Review

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

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