Urban renewal planning and development are vital for enhancing the living quality of city residents. However, such improvement activities are often expensive, time-consuming, and in need of standardization. The convergence of remote sensing technologies, social big data, and artificial intelligence solutions has created unprecedented opportunities for comprehensive digital planning and analysis in urban renewal development and management. However, fast interdisciplinary development imposes some challenges because the data collected and the solutions built are defined piece by piece and require further fusion and integration of knowledge, evaluation standards, systematic analyses, and new methodologies. To address these challenges, we propose a municipal and urban renewal development index (MUDI) system with data modeling and mathematical analysis models. The MUDI system is applied and studied in three circumstances: (1) at regional level, 337 cities are selected in China to demonstrate the MUDI system’s comparable analysis capabilities on a large scale across cities; (2) at city level, 285 residential communities are selected in Xiamen to demonstrate the use of remote sensing data as key MUDIs for a temporal urban land change analysis; and (3) at the level of residential neighborhoods’ urban renewal practices, Xiamen’s Yingping District is selected to demonstrate the MUDI system’s project management capabilities. We find that the MUDI system is highly effective in municipal and urban data model building through the abstraction and summation of grid-based satellite and social big data. Secondly, the MUDI system enables comprehension of the high dimensionality and complexity of multisource datasets for municipal and urban renewal development. Thirdly, the system is applied to enable the use of the newly developed UMAP algorithm, a model based on Riemannian geometry and algebraic topology, and the carrying out of a principal component analysis for the key dimensions and an index correlation analysis. Fourthly, various artificial intelligence-driven algorithms can be developed for urban renewal analyses based on the MUDIs. The MUDI system is a new and effective method for urban renewal planning and management that can be flexibly extended and applied to various cities and urban districts.
For estimating ground object height from single remote sensing images, this study proposes a land use knowledge guided multiscale height estimation network (LUMNet), which takes single image and land use data as inputs and produces an estimated height map as output. First, in the encoder part, the visual geometry group network (VGGNet) is used to extract multilevel deep semantic features from images. Second, the features of the encoder part are fused with those of the decoder part through skip connection with land use knowledge attention weight and Dense Atrous Spatial Pyramid Pooling (DenseASPP). Third, in the decoder part, the features are decoded using upsampling and convolution, and a joint loss function is constructed to supervise network training. Experiments show that the proposed method achieves the best visualizations and quantitative evaluation results among all the tested methods. For the Vaihingen dataset, the RMSE , MAE , and ZNCC of LUMNet are 1.523, 0.969, and 0.908, respectively. For the Potsdam dataset, these are 2.158, 1.222, and 0.871, respectively. The source code of LUMNet has been made public at the following link: https://figshare.com/s/eaf206879ade35a61f88.
Credible historical land use/cover data are very important for past global change research. This study generates a set of integrated reconstruction methods based on multisource data and produces a new set of improved historical cropland data sets in Europe over the past 200 years. For AD 2000, FAO data, existing research results and statistical data are integrated. For AD 1900, a method of integrating two sets of independent historical agricultural land data by correction and validation and supplemented by other historical cropland data are developed. For AD 1850 and 1800, a methodological scheme of diversified proxy integrative technology and methods based on multisource data is constructed. In this new data set, quantitative reconstructions for AD 1900, 1850 and 1800 are improved to account for 100, 78 and 57% of all European countries, respectively. The reconstruction results show that each region in Europe has been in different stage of historical agricultural development. More than 86% of the countries’ cropland area and its proportion peaked in AD 1900 or did not exceed the data for AD 2000. Specifically, a high reclamation zone gradually formed from France to Ukraine, in which every country’s cropland fraction was ≥40% during AD 1800–1900. From AD 1900–2000, the highly cultivated region contracted, and the centre of higher cropland proportions shifted to eastern Europe and Poland, Czechia and Hungary. The cropland area was systematically underestimated by HYDE3.2, with a relative difference ratio of −20 to −30% between HYDE3.2 and this study. Historical empirical data were used for only 32% of countries in HYDE3.2. This method of multiproxy integrated reconstruction is applicable to other regions of the world and it would be worth attempting to apply it to earlier historical European cropland data sets in the future.
Surface soil moisture (SM), as a crucial ecological element, is significant to monitor in semiarid mining areas characterized by aridity and little rainfall. The passive microwave remote sensing, which is not affected by weather, provides more accurate SM information, but the resolution is too coarse for mining areas. The existing downscaling method is usually pointed to natural scenarios like agricultural fields rather than mining areas with high-intensity mining. In this paper, combined with geoinformation related to SM, we designed a convolutional neural network (SM-Residual Dense Net, SM-RDNet) to downscale SMAP/Sentinel-1 Level-2 radiometer/radar soil moisture data (SPL2SMAP_S SM) into 10 m spatial resolution. Based on the in-site measured data, the root mean square error (RMSE) was utilized to verify the downscaling accuracy of SM-RDNet. In addition, we analyzed its performance for different data combinations, vegetation cover types and the advantages compared with random forest (RF). Experimental results show that: (1) The downscaling from the 3 km product with the combination of auxiliary data NDVI + DEM + slope performs best (RMSE 0.0366 m3/m3); (2) Effective data combinations can improve the downscaling accuracy at the range of 0.0477–0.1176 m3/m3 (RMSE); (3) The SM-RDNet shows better spatial completeness, details and accuracy than RF (RMSE improved by 0.0905 m3/m3). The proposed SM-RDNet can effectively obtain the fine-grained SM in semiarid mining areas. Our method bridges the gap between coarse-resolution microwave SM products and ecological applications of small-scale mining areas, and provides data and technical support for future research to explore how the mining effect SM in semiarid mining areas.
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