Abstract
Abstract. The automation of geoinformation (GI) collection and interpretation has been a fundamental goal for many researchers. The developments in various sensors, platforms, and algorithms have been contributing to the achievement of this goal. In addition, the contributions of citizen science (CitSci) and volunteered geographical information (VGI) concepts have become evident and extensive for the geodata collection and interpretation in the era where information has the utmost importance to solve societal and environmental problems. The web- and mobile-based Geographical Information Systems (GIS) have facilitated the broad and frequent use of GI by people from any background, thanks to the accessibility and the simplicity of the platforms. On the other hand, the increased use of GI also yielded a great increment in the demand for GI in different application areas. Thus, new algorithms and platforms allowing human intervention are immensely required for semi-automatic GI extraction to increase the accuracy. By integrating the novel artificial intelligence (AI) methods including deep learning (DL) algorithms on WebGIS interfaces, this task can be achieved. Thus, volunteers with limited knowledge on GIS software can be supported to perform accurate processing and to make guided decisions. In this study, a web-based geospatial AI (GeoAI) platform was developed for map updating by using the image processing results obtained from a DL algorithm to assist volunteers. The platform includes vector drawing and editing capabilities and employs a spatial database management system to store the final maps. The system is flexible and can utilise various DL methods in the image segmentation.
Highlights
The spatial and semantic updating of geodatabases containing land use land cover (LULC) information is a crucial process to ensure their usability
The developed geospatial artificial intelligence (GeoAI) platform principally requires a vector file containing building boundaries to be employed as ground-truth and a single airborne image to be used in the deep learning (DL)-based image segmentation
The main idea behind this study is to demonstrate how DL can assist the tasks for change detection based map updating
Summary
The spatial and semantic updating of geodatabases containing land use land cover (LULC) information is a crucial process to ensure their usability. Many state-of-the-art DL architectures (Ronneberg et al, 2015; He et al, 2017; Chen et al, 2018) have shown outstanding performances in the segmentation/classification tasks, if sufficiently enough training datasets are supplied to the DL architecture Their outputs are still not been frequently utilised or preferred for updating a geodatabase in an end-to-end framework. Facilitating the DL techniques for updating a geodatabase would noticeably empower such interpretation tasks mostly done by manual processing, for revealing areas with change In this way, the time and personnel costs required can be reduced significantly, and such interactive approaches will ensure more accurate and semantically correct data in a relatively shorter amount of time. A geospatial artificial intelligence (GeoAI) supported by a WebGIS platform was designed and implemented to demonstrate how DL can aid geodatabase updating especially for LULC data.
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