Ecosystem balance is an important factor that affects healthy and sustainable urban development. The traditional cellular automata (CA) model considers only a few ecological factors, however, the MCR model can account for ecological factors. In previous studies, few ecological factors were added to the CA model. Thus, the minimal cumulative resistance (MCR) model is combined with the CA and Markov models for the simulation of urban expansion. To verify the reliability of the method, the Wuhan metropolitan area was selected as a representative urban area, and its expansion in the past and future was simulated. Firstly, seven influential factors were selected from the perspective of location theory. The transformation rules of the comprehensive resistance surface followed by the modified CA–Markov model were constructed on the basis of the MCR model. The expansion of the Wuhan metropolitan area in 2013 was simulated on the basis of the 1996 and 2006 maps of land-use status, and the kappa coefficient was used as an index to evaluate the accuracy of the proposed method. Then, the expansion of the Wuhan metropolitan area in 2020 was simulated. Finally, the simulation results obtained with and without the MCR model were compared and analysed from the macro- and micro levels. Results show that the prediction accuracy of the two models differed for ecological regions, such as woodlands and water bodies. The similarities between the regions that were overestimated and underestimated by the MCR-modified CA–Markov model and non-MCR model may be attributed to solution of the land-use transfer matrix with the Markov model. The accuracy of the MCR-modified CA–Markov model for predicting forests, water and other ecological regions was higher than that of the Markov model. Therefore, the proposed MCR-modified CA–Markov model has potential applications in environmentally-conscious urban expansion.

In recent years, because of highly developed LiDAR (Light Detection and Ranging) technologies, there has been increasing demand for 3D change detection in urban monitoring, urban model updating, and disaster assessment. In order to improve the effectiveness of 3D change detection based on point clouds, an approach for 3D change detection using point-based comparison is presented in this paper. To avoid density variation in point clouds, adaptive thresholds are calculated through the k-neighboring average distance and the local point cloud density. A series of experiments for quantitative evaluation is performed. In the experiments, the influencing factors including threshold, registration error, and neighboring number of 3D change detection are discussed and analyzed. The results of the experiments demonstrate that the approach using adaptive thresholds based on local point cloud density are effective and suitable.

This paper discusses the integration of a geographic information system (GIS) and moving objects in surveillance videos (“moving objects” hereinafter) by using motion detection, spatial mapping, and fusion representation techniques. This integration aims to overcome the limitations of conventional video surveillance systems, such as low efficiency in video searching, redundancy in video data transmission, and insufficient capability to position video content in geographic space. Furthermore, a model for integrating GIS and moving objects is established. The model includes a moving object extraction method and a fusion pattern for GIS and moving objects. From the established integration model, a prototype of GIS and moving objects (GIS–MOV) system is constructed and used to analyze the possible applications of the integration of GIS and moving objects.

Rainfall data have a profound significance for meteorology, climatology, hydrology, and environmental sciences. However, existing rainfall observation methods (including ground-based rain gauges and radar-/satellite-based remote sensing) are not efficient in terms of spatiotemporal resolution and cannot meet the needs of high-resolution application scenarios (urban waterlogging, emergency rescue, etc.). Widespread surveillance cameras have been regarded as alternative rain gauges in existing studies. Surveillance audio, through exploiting their nonstop use to record rainfall acoustic signals, should be considered a type of data source to obtain high-resolution and all-weather data. In this study, a method named parallel neural network based on attention mechanisms and multi-scale fusion (PNNAMMS) is proposed for automatically classifying rainfall levels by surveillance audio. The proposed model employs a parallel dual-channel network with spatial channel extracting the frequency domain correlation, and temporal channel capturing the time-domain continuity of the rainfall sound. Additionally, attention mechanisms are used on the two channels to obtain significant spatiotemporal elements. A multi-scale fusion method was adopted to fuse different scale features in the spatial channel for more robust performance in complex surveillance scenarios. In experiments showed that our method achieved an estimation accuracy of 84.64% for rainfall levels and outperformed previously proposed methods.

This work discusses the integration of multi-camera video moving objects (MCVO) and GIS. This integration was motivated by the characteristics of multi-camera videos distributed in the urban environment, namely, large data volume, sparse distribution and complex spatial–temporal correlation of MCVO, thereby resulting in low efficiency of manual browsing and retrieval of videos. To address the aforementioned drawbacks, on the basis of multi-camera video moving object extraction, this paper first analyzed the characteristics of different video-GIS Information fusion methods and investigated the integrated data organization of MCVO by constructing a spatial–temporal pipeline among different cameras. Then, the conceptual integration model of MCVO and GIS was proposed on the basis of spatial mapping, and the GIS-MCVO prototype system was constructed in this study. Finally, this study analyzed the applications and potential benefits of the GIS-MCVO system, including a GIS-based user interface on video moving object expression in the virtual geographic scene, video compression storage, blind zone trajectory deduction, retrieval of MCVO, and video synopsis. Examples have shown that the integration of MCVO and GIS can improve the efficiency of expressing video information, achieve the compression of video data, rapidly assisting the user in browsing video objects from multiple cameras.