Geospasial Intelijen Menggunakan Teknologi Satelit untuk Identifikasi Potensi Sumber Daya Energi dan Meningkatkan Keamanan Nasional
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Nov 27, 2024
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Abstract
Geospatial intelligence all activities involved in planning, collecting, processing, analyzing, exploiting, and disseminating spatial information obtained to gain intelligence about the national security or operational environment, and providing visual representation this knowledge. Geospatial intelligence plays important role in disaster monitoring by providing critical insight information that supports disaster risk reduction and management. Addition to monitoring disasters geospatial intelligence indispensable for monitoring natural resources and potential of each geographic area to optimize all natural resources. Last also plays role in law enforcement, for example the sea where sea is monitored and found violations can harm many parties so that the importance of geospatial intelligence is very helpful to various parties of course also for defense, state security. This research is a systematic literature review that explains the role of geospatial intelligence dealing with military and non-military threats, namely Geospatial Information Systems combined with NASA MERRA-2 hourly global wind speed data and DTU Global Wind Atlas that spatially resolved to characterize the potential for onshore wind energy globally in order to optimally obtain the potential for geography in non-military threats, the concept of military operations aims to secure a country's sea by involving various technologies and intelligence to protect rights and resources
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Haloho, L. S., Martha, S., Adang Supriyadi, A., & Sadewa, A. H. (2024). Geospasial Intelijen Menggunakan Teknologi Satelit untuk Identifikasi Potensi Sumber Daya Energi dan Meningkatkan Keamanan Nasional. Journal on Education, 7(1), 7468 -7481. https://doi.org/10.31004/joe.v7i1.7479
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References
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Butt, M. A. (2015). DEVELOPMENT OF A WEB-BASED COLLABORATIVE PPGIS TO SUPPORT MUNICIPAL PLANNING By.
Dangermond, J., & Goodchild, M. F. (2020). Building geospatial infrastructure. Geo-Spatial Information Science, 23(1), 1–9. https://doi.org/10.1080/10095020.2019.1698274
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57(August), 0–1. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Erskine, M. A., Gregg, D. G., Karimi, J., & Scott, J. E. (2015). Geospatial Reasoning Ability: Definition, Measurement and Validation. International Journal of Human-Computer Interaction, 31(6), 402–412. https://doi.org/10.1080/10447318.2015.1034551
Euchner, J. (2018). The Internet of Things. Research Technology Management, 61(5), 10–11. https://doi.org/10.1080/08956308.2018.1495971
Fernandez Arguedas, V., Pallotta, G., & Vespe, M. (2018). Maritime Traffic Networks: From Historical Positioning Data to Unsupervised Maritime Traffic Monitoring. IEEE Transactions on Intelligent Transportation Systems, 19(3), 722–732. https://doi.org/10.1109/TITS.2017.2699635
Fleming, S., Jordan, T., Madden, M., Usery, E. L., & Welch, R. (2009). GIS applications for military operations in coastal zones. ISPRS Journal of Photogrammetry and Remote Sensing, 64(2), 213–222. https://doi.org/10.1016/j.isprsjprs.2008.10.004
Gao, Y., Duo, H., Che, J., Zhao, S., & Zhao, B. (2023). Research on Efficient Indexing of Large-Scale Geospatial Data Based on Multi-Level Geographic Grid. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10(1-W1-2023), 73–80. https://doi.org/10.5194/isprs-annals-X-1-W1-2023-73-2023
Gao, Y., Lui, J., Liu, Y., Zhai, Z., Che, J., Li, H., Wang, R., & Liu, J. (2023). Research on Natural Resources Spatio-Temporal Big Data Analysis Platform for High Performance Computing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 48(M-1–2023), 115–121. https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-115-2023
Germond, B. (2015). The geopolitical dimension of maritime security. Marine Policy, 54, 137–142. https://doi.org/10.1016/j.marpol.2014.12.013
Harrison-Atlas, D., Maclaurin, G., & Lantz, E. (2021). Spatially-Explicit Prediction of Capacity Density Advances Geographic Characterization of Wind Power Technical Potential. Energies, 14(12), 3609. https://doi.org/10.3390/en14123609
Ivić, M. (2019). Artificial intelligence and geospatial analysis in disaster management. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(3/W8), 161–166. https://doi.org/10.5194/isprs-archives-XLII-3-W8-161-2019
Jamison, T. (2018). Geospatial intelligence and the U.S. Exploring Expedition (1838–1842): reframing the history of U.S. Naval Intelligence. Intelligence and National Security, 33(5), 705–715. https://doi.org/10.1080/02684527.2018.1442995
Jing, C., Du, M., Li, S., & Liu, S. (2019). Geospatial dashboards for monitoring smart city performance. Sustainability (Switzerland), 11(20). https://doi.org/10.3390/su11205648
Jones, A., Koehler, S., Jerge, M., Graves, M., King, B., Dalrymple, R., Freese, C., & Von Albade, J. (2023). BATMAN: A Brain-like Approach for Tracking Maritime Activity and Nuance. Sensors, 23(5). https://doi.org/10.3390/s23052424
Kumar, D., & Shekhar, S. (2014). Photovoltaic Energy Asessment Using Geospatial Technology. Internation Journal of Scientific & Technology Research, 3(6), 54–60. http://www.ijstr.org/final-print/june2014/Photovoltaic-Energy-Asessment-Using-Geospatial-Technology.pdf.
Lary, D. J., Alavi, A. H., Gandomi, A. H., & Walker, A. L. (2016). Machine learning in geosciences and remote sensing. Geoscience Frontiers, 7(1), 3–10. https://doi.org/10.1016/j.gsf.2015.07.003
Lee, J. G., & Kang, M. (2015). Geospatial Big Data: Challenges and Opportunities. Big Data Research, 2(2), 74–81. https://doi.org/10.1016/j.bdr.2015.01.003
Li, W., & Hsu, C. Y. (2022). GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography. ISPRS International Journal of Geo-Information, 11(7). https://doi.org/10.3390/ijgi11070385
Li, W., Hsu, C. Y., & Hu, M. (2021). Tobler’s First Law in GeoAI: A Spatially Explicit Deep Learning Model for Terrain Feature Detection under Weak Supervision. Annals of the American Association of Geographers, 111(7), 1887–1905. https://doi.org/10.1080/24694452.2021.1877527
Lim, G. J., Cho, J., Bora, S., Biobaku, T., & Parsaei, H. (2018). Models and computational algorithms for maritime risk analysis: a review. Annals of Operations Research, 271(2), 765–786. https://doi.org/10.1007/s10479-018-2768-4
Meddens, A. J. H., Steen-Adams, M. M., Hudak, A. T., Mauro, F., Byassee, P. M., & Strunk, J. (2022). Specifying geospatial data product characteristics for forest and fuel management applications. Environmental Research Letters, 17(4). https://doi.org/10.1088/1748-9326/ac5ee0
Medina-Lopez, E., McMillan, D., Lazic, J., Hart, E., Zen, S., Angeloudis, A., Bannon, E., Browell, J., Dorling, S., Dorrell, R. M., Forster, R., Old, C., Payne, G. S., Porter, G., Rabaneda, A. S., Sellar, B., Tapoglou, E., Trifonova, N., Woodhouse, I. H., & Zampollo, A. (2021). Satellite data for the offshore renewable energy sector: Synergies and innovation opportunities. Remote Sensing of Environment, 264(May), 112588. https://doi.org/10.1016/j.rse.2021.112588
Mentri ESDM. (2024). Peraturan Menteri Energi Dan Sumber Daya Mineralrepublik Indonesianomor 2 Tahun 2024 Tentang Pembangkit Listrik Tenaga Surya Atap Yang Terhubung Pada Jaringan Tenaga Listrik Pemegang Izin Usaha Penyediaan Tenaga Listrik Untuk Kepentingan Umum. Mentri Energi Dan Sumber Daya Mineral, 2024, 1–35. https://jdih.esdm.go.id/storage/document/Permen ESDM Nomor 2 Tahun 2024.pdf
Merchant, J. (2007). Using geospatial data in geographic education. Journal of Geography, 106(6), 215–216. https://doi.org/10.1080/00221340701863840
Mohammad Bagher, A. (2015). Types of Solar Cells and Application. American Journal of Optics and Photonics, 3(5), 94. https://doi.org/10.11648/j.ajop.20150305.17
Nguyen, D., Vadaine, R., Hajduch, G., Garello, R., & Fablet, R. (2022). GeoTrackNet - A Maritime Anomaly Detector Using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5655–5667. https://doi.org/10.1109/TITS.2021.3055614
Pierdicca, R., & Paolanti, M. (2022). GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data. Geoscientific Instrumentation, Methods and Data Systems, 11(1), 195–218. https://doi.org/10.5194/gi-11-195-2022
Ruiz, P., Nijs, W., Tarvydas, D., Sgobbi, A., Zucker, A., Pilli, R., Jonsson, R., Camia, A., Thiel, C., Hoyer-Klick, C., Dalla Longa, F., Kober, T., Badger, J., Volker, P., Elbersen, B. S., Brosowski, A., & Thrän, D. (2019). ENSPRESO - an open, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials. Energy Strategy Reviews, 26(October 2018), 100379. https://doi.org/10.1016/j.esr.2019.100379
Salehi, H., & Burgueño, R. (2018). Emerging artificial intelligence methods in structural engineering. In Engineering Structures (Vol. 171, Issue Mi). https://doi.org/10.1016/j.engstruct.2018.05.084
Satapathy, D. R., Katpatal, Y. B., & Wate, S. R. (2008). Application of geospatial technologies for environmental impact assessment: An Indian Scenario. International Journal of Remote Sensing, 29(2), 355–386. https://doi.org/10.1080/01431160701269002
Shahabi, C., Banaei-Kashani, F., Khoshgozaran, A., Nocera, L., & Xing, S. (2010). GeoDec: A Framework to Visualize and Query Geospatial Data for Decision-Making. IEEE Multimedia, 17(3), 14–23. https://doi.org/10.1109/MMUL.2010.5692179
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