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Monitoring the atmospheric CO 2 columns inside and around a city is of great importance to understand the temporal–spatial variation of XCO 2 near strong anthropogenic emissions. In this study, we use two FTIR CO 2 column measurements in Beijing (Bruker EM27/SUN) and Xianghe (Bruker IFS 125HR) between 2019 and 2021 to investigate the differences of XCO 2 between Beijing (urban) and Xianghe (suburb) in North China and to validate the OCO-2 and OCO-3 satellite XCO 2 retrievals. The mean and standard deviation (std) of the Δ XCO 2 between Beijing and Xianghe (Beijing–Xianghe) observed by two FTIR instruments are 0.206 ± 1.736 ppm, which has a seasonal variation and varies with meteorological conditions (wind speed and wind direction). The mean and std of the XCO 2 differences between co-located satellite and FTIR measurements are −0.216 ± 1.578 ppm in Beijing and −0.343 ± 1.438 ppm in Xianghe for OCO-2 and 0.637 ± 1.594 ppm in Beijing and 1.206 ± 1.420 ppm in Xianghe for OCO-3. It is found that the OCO-3 snapshot area mode (SAM) measurements can capture the spatial gradient of XCO 2 between urban and suburbs well. However, the FTIR measurements indicate that the OCO-3 SAM measurements are about 0.9–1.4 ppm overestimated in Beijing and Xianghe. [ABSTRACT FROM AUTHOR]

After Beijing wins the bit to host the 29th Olympic Games, in order to manifest the technical support advantages and capabilities of the autonomously-developed RS and GIS based change detection techniques in 2008 Beijing Olympic Games, and from the standpoints of executing new city planning, relieving the traffic congestion as well as maintaining the historic features, an automatic satellite monitoring system has been studied and established to accomplish the mission of unauthorized construction inspection within the Sixth Ring Road of Beijing city quarterly, by adopting the CBERS-2 satellite images and combining technologies of GIS, GPS, etc. This article discusses the applicable procedures and key issues when utilizing such Chinese satellite images and relevant techniques to discover the illegal constructions, and introduces the monitoring system from both the design and implementation aspects; additionally, some typical application cases in the practice of the system are also illustrated. The monitoring system can timely supply abundant information to facilitate the policy-making of relevant planning departments, thus providing consolidate technical support to eliminate the illegal constructing behaviors in the blossom. During the five years' excellent performance, it has helped China save large amounts of expenditures for processing of unauthorized constructed buildings. [ABSTRACT FROM AUTHOR]

The segmentation of high-resolution (HR) remote sensing images is very important in modern society, especially in the fields of industry, agriculture and urban modelling. Through the neural network, the machine can effectively and accurately extract the surface feature information. However, using the traditional deep learning methods requires plentiful efforts in order to find a robust architecture. In this paper, we introduce a neural network architecture search (NAS) method, called NAS-HRIS, which can automatically search neural network architecture on the dataset. The proposed method embeds a directed acyclic graph (DAG) into the search space and designs the differentiable searching process, which enables it to learn an end-to-end searching rule by using gradient descent optimization. It uses the Gumbel-Max trick to provide an efficient way when drawing samples from a non-continuous probability distribution, and it improves the efficiency of searching and reduces the memory consumption. Compared with other NAS, NAS-HRIS consumes less GPU memory without reducing the accuracy, which corresponds to a large amount of HR remote sensing imagery data. We have carried out experiments on the WHUBuilding dataset and achieved 90.44% MIoU. In order to fully demonstrate the feasibility of the method, we made a new urban Beijing Building dataset, and conducted experiments on satellite images and non-single source images, achieving better results than SegNet, U-Net and Deeplab v3+ models, while the computational complexity of our network architecture is much smaller. [ABSTRACT FROM AUTHOR]

Automatically monitoring newly constructed building areas (NCBAs) is essential for efficient land resource management and sustainable urban development, particularly in the rapidly urbanizing country of China. In this regard, time-series multi-view high-resolution optical satellite images can provide fine spatial details for clearly characterizing NCBAs, but this leads to great heterogeneity and complexity, owing to the high spectral variation, complicated imaging conditions, and different viewing angles. Moreover, to date, the vertical features and time-series information from these images have not been fully exploited for urban change detection. In this paper, our primary objective is to automatically detect the presence of NCBAs, and meanwhile, to investigate the feasibility of identifying their change timing using time-series multi-view ZY-3 high-resolution satellite images. To this aim, we propose an automatic change detection method consisting of three components: 1) firstly, we jointly use planar-vertical features to delineate the NCBAs; 2) object-based temporal correction is subsequently applied to improve the spatiotemporal consistency of the features; and 3) finally, a multi-temporal change detection model is used to simultaneously capture the NCBAs and the change timing. We applied the method on two urban fringe areas of Beijing (7 multi-temporal image sets) and Shanghai (7 multi-temporal image sets), respectively, which are cities that have been experiencing rapid urbanization. The experimental results confirmed the effectiveness of the proposed method. For both study areas, the F-score values reached nearly 90% in terms of NCBA detection, and with respect to the change timing, the overall accuracies with a one-year tolerance strategy reached around 92%. The joint use of the planar-vertical features and the inclusion of multi-temporal images make the proposed method a promising approach for automatically providing the spatiotemporal information of NCBAs in practical applications. • An automatic method for monitoring newly constructed building areas (NCBAs) • Time-series multi-view high-resolution optical satellite images were adopted • Joint use of planar-vertical features for delineating NCBAs • Object-based temporal correction for improving spatiotemporal consistency • Multi-temporal change detection for deriving change location and timing [ABSTRACT FROM AUTHOR]