Remote sensing technology for disaster mitigation and regional infrastructure planning in urban area: a review

Abstract

A Very high intensity of regional development is ubiquitous in urban areas. Therefore, urban development requires a proper spatial development strategy in many facets, especially social aspect and disaster potential. The essence of social aspect lies in the prevailing norms and local wisdom that have long existed and become the basis of community life. Inducing various effects on infrastructure development, disaster potential has to be considered as well. Disaster mitigation measures can start with the use of continually developing remote sensing technology, which provides a basis for preparing sustainable development planning. The realization of these measures in urban areas demands specific adjustment to the environmental conditions. This study aimed to examine the capacity of remote sensing data to support disaster mitigation and infrastructure planning based on energy conservation in urban areas. The results indicate that remote sensing technology can be an option for sustainable development planning in urban areas.

Geological aspect analysis for micro hydro power plant site selection based on remote sensing data

“…The combination of NIR and SWIR produces optimal reflection against rocks so that rock type information can be recognized well, while the variation of visible and NIR Bands is applied to extract information about vegetation for making land use maps and estimating water discharge [44]- [46]. Geospatial data generated from remote sensing analysis can be used widely in various fields, one of which is disaster mitigation of the infrastructure to be built [47]. This study emphasizes the interpretation of satellite imagery to obtain geological conditions, morphology, and potential areas for landslides and other disasters.…”

Risk management system for the monitoring and control of a small lettuce greenhouse

“…Within the problem, the little technification in crops was identified [13] especially in medium and small ones, where the use of technologies [14] for the monitoring and automation of crops is low [15] affecting the competitiveness of producers [16], [17].…”

Satellite image inpainting with deep generative adversarial neural networks

“…Satellite programs such as landsat [9], sentinel, aqua/terra, among others, provide a wealth of freely available data sets for the masses. This tremendous progress has unlocked many innovations that extract valuable insights [10][11] from satellite imagery using big data pipelines and advanced machine learning systems [12].…”