The long time series and consistent “urban core-suburban-rural” (USR) structure in a city region is essential to understanding urban–suburban–rural interaction and urbanization pathways. It is always considered to be a single land use type (e.g., impervious area) in remote sensing research. The long-term (1992–present) nighttime light (NTL) data of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) provide the potential for retrieving time series of USR structure. In this study, we propose an improved approach to mapping the USR structure of the three subcategories based on a heuristic algorithm of Mann–Kendall mutation detection on the NTL quantile curve. First, a minor adjustment of VIIRS NTL is applied for matching the value ranges of DMSP NTL data and keeping the advantage of VIIRS to generate a long-term NTL dataset. Second, the heuristic algorithm of Mann–Kendall mutation detection is processed to find two optimal thresholds in the NTL quantile curve, which is used for USR extraction. Finally, a temporal consistency check is used to post-process the initial USR area for obtaining a more consistent and reliable USR sequence. To evaluate the performance of the proposed method, we retrieved the USR structures of 19 typical cities in China from 1992 to 2020 based on NTL datasets. The evaluations of spatiotemporal consistency compared with the validation data indicate that the USR retrieval results show good agreement with the land use map derived from Landsat images and the time series product from MODIS. The average overall accuracy (OA) of overall urban extent is higher than 0.95 and the average kappa coefficient (KC) reaches 0.6. Moreover, we investigated the urban dynamics and USR interactions of 19 cities from 1992 to 2020. Overall, this study proposes an improved approach for long-term USR mapping from NTL images at a regional scale and it will provide a valuable method for urbanization dynamics analysis.

Outfalls into rivers are the final gate of anthropogenic pollution flowing to receiving waters, which means that outfall surveys are significant to basin environmental protection and ecosystem health management. Unmanned aircraft systems (UAS) with high spatial resolution imagery have become important data for ongoing surveys of outfalls. However, outfalls retrieval from UAS imagery is inefficient to visual interpretation and a challenging task for traditional spectral-based and object-oriented classification methods given the problems of salt-and-pepper noise and scale selection. In this study, an improved geo-deep learning approach based on the faster region convolutional neural network (R-CNN) architecture (GDCNN-outfalls) is proposed for retrieving outfalls into rivers with UAS imagery. In the proposed method, three tactics—anchor size, region of interest (RoI), and hard negative mining—were adopted to optimize the benchmark Faster R-CNN application in outfalls retrieval. Meanwhile, a geo-classifier module with digital surface model (DSM) enhancement and a spatial activation function was integrated with the Faster R-CNN architecture to generate GDCNN-outfalls. The validation experiments indicated that GDCNN-outfalls improved the performance of Faster R-CNN in outfall retrieval by suppressing false positive (FPs) from 33.52% to 26.14% and increasing the F1 score from 0.72 to 0.75. The test results confirm the performance of GDCNN-outfalls with a recall of 79.3% and higher precision (48.4%) than that of Faster R-CNN (2.1%), also show the GDCNN-outfalls is ten times faster than visual interpretation. This study demonstrates that the combination of deep learning and UAS techniques can be a feasible solution to detect outfalls in outfall surveys.

Accurate and timely information on the “core urban-suburban-rural” (USR) spatial structure in a metropolitan region is significant for both the scientific and policy-making communities. However, USR is usually considered as a single land use type, such as an impervious area, rather than three combined subcategories in remote-sensing image retrieval, especially for suburban areas, which obscures the details of the urbanization process. In this paper, we propose a quantile approach to retrieve the structure of USR based on stable nighttime light (NTL) data from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) and apply it in the Beijing-Tianjin-Hebei (JJJ) of China from 1995 to 2013. The key parameters of the NTL threshold, which is the maximum change point of the NTL intensity at the USR boundary, used to retrieve the three subcategories of USR are automatically defined based on the quantile approach with three iterations. Then, the overall accuracy and consistency of the retrieval results are evaluated using the corresponding visual interpretation map from Landsat images with a 30 m resolution. Moreover, the influence of parameter uncertainty is compared by introducing the human settlement index (HSI). According to the time-series analysis of USR retrieval in this study, the JJJ experienced rapid urbanization from 1995 to 2013, with the core urban area expanding by 7098 km² (average increase of 2.7 times), the suburban area expanding by 12,690 km² (average increase of 2.8 times), and the rural area increasing by 4986 km² (average increase of 0.38 times). The USR results retrieved based on the approach agree well with the validation of the visual interpretation map, with an overall accuracy (OA) of 0.904 and a kappa coefficient (KC) of 0.650 at the city level. The USR result with the HSI as the input shows that NTL is more suitable for USR structure retrieval as the NTL shows less uncertainty compared with other parameters such as the vegetation index (VI). This study proposes an improved quantile approach for USR mapping from NTL images on a regional scale, which will provide a useful method for urbanization dynamics analysis.