Search

High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding. However, few datasets can be used for land-use/land-cover (LULC) classification, binary change detection (BCD) and semantic change detection (SCD) simultaneously because classification datasets always have one time phase and BCD datasets focus only on the changed location, ignoring the changed classes. Public SCD datasets are rare but much needed. To solve the above problems, a tri-temporal SCD dataset made up of Gaofen-2 (GF-2) remote sensing imagery (with 11 LULC classes and 60 change directions) was built in this study, namely, the Wuhan Urban Semantic Understanding (WUSU) dataset. Popular deep learning based methods for LULC classification, BCD and SCD are tested to verify the reliability of WUSU. A Siamese-based multi-task joint framework with a multi-task joint loss (MJ loss) named ChangeMJ is proposed to restore the object boundaries and obtains the best results in LULC classification, BCD and SCD, compared to the state-of-the-art (SOTA) methods. Finally, a large spatial-scale mapping for Wuhan central urban area is carried out to verify that the WUSU dataset and the ChangeMJ framework have good application values.

High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding. However, few datasets can be used for land-use/land-cover (LULC) classification, binary change detection (BCD) and semantic change detection (SCD) simultaneously because classification datasets always have one time phase and BCD datasets focus only on the changed location, ignoring the changed classes. Public SCD datasets are rare but much needed. To solve the above problems, a tri-temporal SCD dataset made up of Gaofen-2 (GF-2) remote sensing imagery (with 11 LULC classes and 60 change directions) was built in this study, namely, the Wuhan Urban Semantic Understanding (WUSU) dataset. Popular deep learning based methods for LULC classification, BCD and SCD are tested to verify the reliability of WUSU. A Siamese-based multi-task joint framework with a multi-task joint loss (MJ loss) named ChangeMJ is proposed to restore the object boundaries and obtains the best results in LULC classification, BCD and SCD, compared to the state-of-the-art (SOTA) methods. Finally, a large spatial-scale mapping for Wuhan central urban area is carried out to verify that the WUSU dataset and the ChangeMJ framework have good application values. [ABSTRACT FROM AUTHOR]

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information. [ABSTRACT FROM AUTHOR]