Abstract
Evolving Earth observation and change detection techniques enable the automatic identification of Land Use and Land Cover Change (LULCC) over a large extent from massive amounts of remote sensing data. It at the same time poses a major challenge in effective organization, representation and modeling of such information. This study proposes and implements an integrated computational framework to support the modeling, semantic and spatial reasoning of change information with regard to space, time and topology. We first proposed a conceptual model to formally represent the spatiotemporal variation of change data, which is essential knowledge to support various environmental and social studies, such as deforestation and urbanization studies. Then, a spatial ontology was created to encode these semantic spatiotemporal data in a machine-understandable format. Based on the knowledge defined in the ontology and related reasoning rules, a semantic platform was developed to support the semantic query and change trajectory reasoning of areas with LULCC. This semantic platform is innovative, as it integrates semantic and spatial reasoning into a coherent computational and operational software framework to support automated semantic analysis of time series data that can go beyond LULC datasets. In addition, this system scales well as the amount of data increases, validated by a number of experimental results. This work contributes significantly to both the geospatial Semantic Web and GIScience communities in terms of the establishment of the (web-based) semantic platform for collaborative question answering and decision-making.
Highlights
The advancement of remote sensing techniques and the Global Earth Observation System of Systems (GEOSS) makes it possible to collect vast amounts of environmental data about the Earth’s surface at a finer resolution
To model this complexity and effectively manage Land Use and Land Cover Change (LULCC) information from time series geospatial data, we propose and implement an integrated computational framework to support the modeling, semantic and spatial reasoning of change information with regard to space, time and topology
This paper introduces the development and implementation of a computational framework to support the modeling, semantic and spatial reasoning of change information with regard to space, time and topology
Summary
The advancement of remote sensing techniques and the Global Earth Observation System of Systems (GEOSS) makes it possible to collect vast amounts of environmental data about the Earth’s surface at a finer resolution. Several techniques [9] can accurately detect LULC changes from remote sensing imagery, there exist great challenges to effectively organize and model such information from multi-temporal remote sensing images. FFoorr tthhee sseeccoonndd ccaassee ((FFiigguurree 11bb)),, llooccaall aattttrriibbuutteess cchhaannggee wwiitthhiinn aann ootthheerrwwiissee gglloobbaallllyy--uunniiffoorrmm oobbjjeecctt. Dtoutehetodythneamdyicnsaomf iLcUs oLfCLCUhLaCngCeh(LanUgLeC(CL)U, sLeCmCa)n, tsiecmreaanstoincirnegasisonalisnognieseadlesod tnoeebdeeadblteotobetraabcelethtoe tervaocleutthioeneovfoalnutaiorenaoafnadnexaprelaoraenidts espxpatlioarle, tietms sppoartailala,ntdemtoppoorlaolgaicnadl rteolpatoiloongsihciaplsrewlaitthionnesahribpys awreitahs n[1e1a]r.bFyinaarlelays, i[t1i1s].crFiitnicaalllyt,hiattisLcUrLitCicCalrtehparteLseUnLtaCtiConreipnrteesgernatteastioknnoinwtleegdrgaetefsrokmnoowthleedrgseoufrrocmes,ostuhcehr saosularcneds,asnudchresaosulracnedpalanndnrinesgoiunrfcoermpalatnionni,nbgeiynofnodrmthaatitoenx,trbaecyteodndfrothmatthexetrreamctoedte fsreonmsinthgeimreamgeortye s[1e2n]s.ing imagery [12]. To model this complexity and effectively manage LULCC information from time series geospatial data (remote sensing imagery), we propose and implement an integrated computational.
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