پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 642 |
| تعداد مقالات | 9,409 |
| تعداد مشاهده مقاله | 68,133,699 |
| تعداد دریافت فایل اصل مقاله | 39,713,498 |
Semantic reasoning system for monitoring natural disasters based on GeoSPARQL ontology and satellite images study cases: Flood-prone areas of Shiraz city | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 26، دوره 16، شماره 1، فروردین 2025، صفحه 319-328 اصل مقاله (936.23 K) | ||
| نوع مقاله: Review articles | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2024.32616.4881 | ||
| نویسندگان | ||
| Nasim Khozouie* 1؛ Zahra Ansarifard2 | ||
| 1Department of Computer Engineering, Faculty of Engineering, Yasouj University, Yasouj, Iran | ||
| 2Department of Computer Engineering, Faculty of Technology and Engineering, Poya Institute of Higher Education, Yasouj, Iran | ||
| تاریخ دریافت: 08 آذر 1402، تاریخ بازنگری: 21 بهمن 1402، تاریخ پذیرش: 09 اسفند 1402 | ||
| چکیده | ||
| Reliable and automatic classification of satellite images is of great importance for disaster management and climate change monitoring. In addition to recognizing objects and entities in satellite images, the ability to reason about these entities and subsequently respond to queries raised by human operators to guide decision-making processes is important. According to recent studies by climate researchers, various parts of Shiraz are exposed to flood risk. Therefore, the Shiraz flood scenario is real. So we have defined a disaster scenario where the central part of Shiraz is mostly covered by water. One of the main goals of this research is to show the geometry of the regions on the map, enabling the map to respond to questions related to the topology and neighborhood of the regions. In this research, we presented a framework to transfer satellite image data to an interactive map that is ready for mining. In order to obtain a searchable map directly from satellite data, a CNN classifier that is sensitive to image features is used to feed this framework with labelled regions. They show their capabilities in terms of route connectivity. Representing such features in an ontology that is an extension of the existing GeoSPARQL ontology enables the system to automatically search for classified areas based on specific criteria of areas, selected based on environmental status. We have shown how by semantically enriching the representation of regions in ontocity, we can enable the system to automatically find options for regions, thus improving search time, including region revision and co-routing. This SemCityMap framework can now be used as a tool for better decision-making and situational awareness. | ||
| کلیدواژه‌ها | ||
| semantic reasoning؛ ontology؛ satellite images؛ monitoring of natural disasters | ||
| مراجع | ||
|
[1] A. Agresta, G. Fattoruso, M. Pollino, F. Pasanisi, C. Tebano, S. De Vito, and G. Di Francia, An ontology framework for flooding forecasting, Computational science and its applications–ICCSA 2014, 14th Int. Conf., Guimaraes, Portugal, Proc., Part IV, Springer International Publishing, 14 (2014), 417–428. [2] A. Bashir, R. Nagpal, D. Mehrotra, and M. Bala, Knowledge representation and information retrieval from ontologies, R. Kumar, A.K. Verma, T.K. Sharma, O.P. Verma and S. Sharma, (eds) Soft computing: Theories and applications, Lecture Notes in Networks and Systems, Springer, Singapore, 627 (2023). [3] R.F. Calhau, T.P. Sales, I. Oliveira, S. Kokkula, L.F. Pires, D. Cameron, G. Guizzardi, and J.P.A. Almeida, A system core ontology for capability emergence modeling, Int. Conf. Enterprise Design, Operations, Comput., Cham: Springer Nature Switzerland, 2023, pp. 3–20. [4] B. Dutta and P.K. Sinha, An ontological data model to support urban flood disaster response, J. Inf. Sci. 0 (2023), 01655515231167297. [5] H. Gu, H. Li, L. Yan, Z. Liu, T. Blaschke, and U. Soergel, An object-based semantic classification method for high resolution remote sensing imagery using ontology, Remote Sens. 9 (2017), no. 4, 329. [6] K. Kalabokidis, N. Athanasis, and M. Vaitis, OntoFire: an ontology-based geo-portal for wildfires, Natural Hazards Earth Syst. Sci. 11 (2011), no. 12, 3157–3170. [7] N. Karthikeyan, I. Gugan, M.S. Kavitha, and S. Karthik, An effective ontology-based query response model for risk assessment in urban flood disaster management, J. Intell. Fuzzy Syst. 44 (2023), no. 3, 5163–5178. [8] M. Langkvist, A. Kiselev, M. Alirezaie, and A. Loutfi, Classification and segmentation of satellite ortho imagery using convolutional neural networks, Remote Sens. 8 (2016), no. 4, 329. [9] A.Y. Lin, S. Arabandi, T. Beale, W.D. Duncan, A. Hicks, W.R. Hogan, M. Jensen, R. Koppel, C. Martınez-Costa, O. Nytro, and J.S. Obeid, Improving the quality and utility of electronic health record data through ontologies, Standards 3 (2023), no. 3, 316–340. [10] M.A. Mostafavi and M. Bakillah, Real time semantic interoperability in AD HOC networks of geospatial data sources: Challenges, achievements and perspectives, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 1 (2012), 217–222. [11] M. Priadarsini and M.K. Dharani, Distributed inference approach on massive datasets using MapReduce, Int. Conf. Comp. Commun. Inf., IEEE, 2023, pp. 1–7. [12] J. Rodrıguez-Revello, C. Barba-Gonzalez, M. Rybinski, and I. Navas-Delgado, KNIT: Ontology reusability through knowledge graph exploration, Expert Syst. Appl. 228 (2023), 120239. [13] R.J. Rovetto, The ethics of conceptual, ontological, semantic and knowledge modeling, AI & Society, Springer, 2023, pp. 1–22. [14] D. Shukla, H.K. Azad, K. Abhishek and S. Shitharth, Disaster management ontology-an ontological approach to disaster management automation, Sci. Rep. 13 (2023), no. 1, 8091. [15] K.J. Sowmiya Narayanan and A. Manimaran, Recent developments in geographic information systems across different application domains: A review, Knowledge Inf. Syst. 66 (2024), no. 3, 1523–1547. [16] X. Xu, Y. Zhong, L. Zhang, and H. Zhang, Sub-pixel mapping based on a MAP model with multiple shifted hyperspectral imagery, IEEE J. Selected Topics Appl. Earth Observ. Remote Sens. 6 (2013), no. 2, 580–593 | ||
|
آمار تعداد مشاهده مقاله: 347 تعداد دریافت فایل اصل مقاله: 249 |
||