Abstract
Abstract. The introduction of airborne Synthetic Aperture Radar (SAR) approach has successfully addressed several challenges for mapping and surveying applications Unlike other conventional sensors, airborne SAR mapping approach offers practicality and significant cost savings for the nation minimizing the need for ground control points on the ground in addition to providing high-resolution, day-and-night, cloud coverage and weather independent images, which in turn provides faster turnaround times for creation of large area geospatial data. Up-to-date building map is necessary to guide the decision making in many fields to understand the urban dynamics such as in disaster management, population estimation, planning and many other applications. Whilst mapping and surveying work using airborne SAR have started to capture many interest among surveyors, professionals and practitioners abroad, Malaysia however is still lacking behind in term of the knowledge and the usage of this technology together with Deep Learning, Machine Learning approach especially in building extraction for topographic mapping and urban planning and development. Deep learning is a subset of the machine learning algorithm. Recently, Deep Learning has been proposed to solve traditional artificial intelligent problems. In order to develop a sustainable national geospatial infrastructure for years to come, the integration between airborne SAR and other sensors as such LIDAR is therefore essential in Malaysia and in high demand for urban planning and management. Thus, this paper reviews current techniques and future trends of multi-sources Remote Sensing for building extraction.
Highlights
Data is the fuel that powers geospatial systems
The aim of this study is to propose a new approach to integrate high resolution aerial imagery and LIDAR data to improve the accuracy of classification using Support Vector Machine algorithm
Resulting from Synthetic Aperture Radar (SAR) capabilities to generate wide-range DEM, the synergy between SAR and multiple sensors has brought in the potential of synoptic viewing and repetitive coverage that is expected to be capable of drastically reducing costs, timelines and improving the accuracy of topographic maps
Summary
Data is the fuel that powers geospatial systems. Acquisition of accurate and reliable data is becoming increasingly important in which sensors and processing approach selection play a signature role. Remote Sensing approach has been increasingly used in Malaysia especially in urban/city planning, monitoring, disaster management, mapping and many more (Abd Mubin et al, 2019; Hamedianfar et al, 2014; Meesuk et al, 2015; Shaharum et al, 2018). Even as the maps are being developed, they are already perversely out-dated, so the financial investment and the value of that investment are completely misaligned This is especially true with groundbased mapping and surveying campaign that historically requires a large sum of expenditure and highly laborious especially towards producing high accuracy elevation models used in creating contours for topographic maps. Remote Sensing object detection plays an important role toward producing an up-to-date map for environmental monitoring, geological hazard detection, land use land cover (LULC) mapping, geographic information system (GIS) update, precision agriculture, and urban planning.
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