Coal fires are a common and serious problem in most coal-bearing countries. Thus, it is very important to monitor changes in coal fires. Remote sensing provides a useful technique for investigating coal fields at a large scale and for detecting coal fires. In this study, the spreading direction of a coal fire in the Wuda Coal Field (WCF), northwest China, was analyzed using multi-temporal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) thermal infrared (TIR) data. Using an automated method and based on the land surface temperatures (LST) that were retrieved from these thermal data, coal fires related to thermal anomalies were identified; the locations of these fires were validated using a coal fire map (CFM) that was developed via field surveys; and the cross-validation of the results was also carried out using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared images. Based on the results from longtime series of satellite TIR data set, the spreading directions of the coal fires were determined and the coal fire development on the scale of the entire coal field was predicted. The study delineated the spreading direction using the results of the coal fire dynamics analysis, and a coal fire spreading direction map was generated. The results showed that the coal fires primarily spread north or northeast in the central part of the WCF and south or southwest in the southern part of the WCF. In the northern part of the WCF, some coal fires were spreading north, perhaps coinciding with the orientation of the coal belt. Certain coal fires scattered in the northern and southern parts of the WCF were extending in bilateral directions. A quantitative analysis of the coal fires was also performed; the results indicate that the area of the coal fires increased an average of approximately 0.101 km² per year.

Currently, the main difficulty in separating the land surface temperature (LST) and land surface emissivity (LSE) from field-measured hyperspectral Thermal Infrared (TIR) data lies in solving the radiative transfer equation (RTE). Based on the theory of wavelet transform (WT), this paper proposes a method for accurately and effectively separating LSTs and LSEs from field-measured hyperspectral TIR data. We show that the number of unknowns in the RTE can be reduced by decomposing and reconstructing the LSE spectrum, thus making the RTE solvable. The final results show that the errors introduced by WT are negligible. In addition, the proposed method usually achieves a greater accuracy in a wet-warm atmosphere than that in a dry-cold atmosphere. For the results under instrument noise conditions (NE∆T = 0.2 K), the overall accuracy of the LST is approximately 0.1–0.3 K, while the Root Mean Square Error (RMSE) of the LSEs is less than 0.01. In contrast to the effects of instrument noise, our method is quite insensitive to noises from atmospheric downwelling radiance, and all the RMSEs of our method are approximately zero for both the LSTs and the LSEs. When we used field-measured data to better evaluate our method’s performance, the results showed that the RMSEs of the LSTs and LSEs were approximately 1.1 K and 0.01, respectively. The results from both simulated data and field-measured data demonstrate that our method is promising for decreasing the number of unknowns in the RTE. Furthermore, the proposed method overcomes some known limitations of current algorithms, such as singular values and the loss of continuity in the spectrum of the retrieved LSEs.

This study presents a novel method for assessing landslide hazards along highways using remote sensing and machine learning. We extract geospatial features such as slope, aspect, and rainfall over Guangxi, China, and apply an extreme gradient boosting model pre-trained on contiguous United States datasets. The model produces susceptibility maps that indicate landslide probability at different scales. However, the lack of accurate data on historical landslides in Guangxi challenges the model evaluation and comparison between regions. To overcome this, we calibrate the model to fit the local conditions in Guangxi. The calibrated model agrees with the observed landslide locations, implying its capability to capture regional variations in landslide mechanisms. We apply the model at a 30 m resolution along the Heba Expressway and validate it against reports from July 2021 to March 2022. The model correctly predicts five of seven landslide events in this period with a reasonable alarm rate. This framework has the potential for large-scale landslide risk management by informing transportation planning and infrastructure maintenance decisions. More data on landslide timing and human disturbance events may improve the model’s accuracy across diverse geographical areas and terrains.

The automatic extraction of road network information from satellite images is a meaningful and challenging task. Particularly, the analysis of road surface materials is very important during transport construction and maintenance. This paper proposes a method to extract road area and identify its corresponding materials. The approach is based on two different convolutional neural network structures. Firstly, we use encoder-decoder symmetric network structure to extract the candidate road area. Then the former outputs is processed by atrous convolutional network with very deep layers, in order to classify the covered substances through their representative spectral features. We also utilize the physical characteristics of road network to design morphology approach to enhance the completeness and formation of the road network. Experiential results on various satellite images show that the method can yields better accuracy and adaptability than other convolutional network based methods.

Automated guided vehicle (AGV) has the advantages of high endurance, autonomy and security, which render them appealing for applications such as monitoring and sensing networks. However, AGVs may be limited in energy and transmission range. Unmanned Aerial Vehicle (UAV) is a promising platform that can assist terrestrial networks. In this work, we aim to adopt a UAV swarm to assist the data forwarding of AGVs and propose a novel data transmission framework based on collaborative beamforming (CB) where the AGVs and UAVs jointly construct a virtual antenna array (VAA) to transmit data to the remote air base stations (ABSs). Specifically, we formulate a terrestrial and air collaboratively data transmission multiobjective optimization problem (TATFMOP) to optimize the excitation current weights and locations of the AGVs and UAVs, and the communication order of different remote ABSs. Since TATFMOP is an NP-hard problem, we present an extended multiobjective ant-lion optimization (EMOALOIB) with opposition-based population initialization and black hole update operators to solve the problem. Simulations results demonstrate that the proposed EMOALOIB outperforms other existing benchmark algorithms and can obtain more valuable solutions.