Economic fruit forest is an important part of Chinese agriculture with high economic value and ecological benefits. Using UAV multi-spectral images to research the classification of economic fruit forests based on deep learning is of great significance for accurately understanding the distribution and scale of fruit forests and the status quo of national economic fruit forest resources. Based on the multi-spectral remote sensing images of UAV, this paper constructed semantic segmentation data of economic fruit forests, conducted a comparative study on the classification and identification of economic fruit forests of FCN, SegNet, and U-Net classic semantic segmentation models, and proposed an improved ISDU-Net model. The recognition accuracies of the ISDU-Net were 87.73%, 70.68%, 78.69%, and 0.84, respectively, in terms of pixel accuracy, average intersection ratio, frequency weight intersection ratio, and Kappa coefficient, which were 3.19%, 8.90%, and 4.51% higher than the original U-Net model. The results showed that the improved ISDU-Net could effectively improve the learning ability of the model, perform better in the prediction of short sample categories, obtain a higher classification accuracy of fruit forest crops, and provide a new idea for the research on accurate fruit forest identification.

Change detection, a critical and flourishing Earth observation technology, aims to identify changes through cross-temporal remote sensing images acquired over the same geographical area. With the widespread use in various change scenarios, it becomes essential to utilize heterogeneous images due to the high challenge of accessing the ideal homogeneous images. Nevertheless, domain shift, generated by different imaging factors (e.g., sensors, seasons, atmosphere, illumination), makes it unable to compare the heterogeneous images directly. To address this problem, we propose a deep domain adaptation and disentangled representation network for unsupervised heterogeneous change detection (DADR-HCD), which bridges the domain gap from the perspective of causal mechanisms and compares the differences in the content feature space. In the training stage, the deep features of the input bitemporal images are further disentangled into the domain-invariant (content) features and domain-specific (style) features through an explicit image translation network. Furthermore, unlike comparing the differences at the image level or deep feature space, the change probability maps are directly calculated based on the content feature similarity in the prediction stage, which minimizes the style noise and avoids the asymmetry of image-level translation. Finally, the binary change maps are obtained using threshold segmentation and morphological post-processing strategies. The comprehensive experimental results and detailed analysis on five typical datasets demonstrate the effectiveness and superiority of the proposed DADR-HCD network in the unsupervised heterogeneous change detection task.

The geological environmental damage caused by coal mining has become a hot issue in current research. Especially in the western mining area, the size of the mining working face is large, the mining intensity is high, while the surface movement and deformation are more intense and wider. Therefore, it is necessary to effectively monitor the surface using appropriate means and carrying out research on the overlying strata structure of the stope. In this paper, by using advantages of various subsidence monitoring technologies and the technical framework of the Internet of Things (IoT), a “space–air–ground” integrated collaborative monitoring network is constructed. The evolution law of overlying strata structure is studied based on discrete element simulations and theoretical analysis. Furthermore, a discrete element mechanical parameter inversion method is proposed. The main results, using numerical simulations, are as follows: The mean square error of monitoring surface subsidence is 33.2 mm, the mean square error of mechanical parameter inversion is 13.4 mm, and relative error is as low as 3.8%. The surface subsidence law of adjacent mining under different working face widths and interval coal pillar widths is revealed. The Boltzmann function model of surface subsidence ratio changing with width–depth ratio and the calculation formula of width reduction coefficient of adjacent mining working face are inverted. The critical failure width of the interval coal pillar is determined as 20.5 m. Based on the theory of “arch–beam” structure and numerical simulation results, the overlying strata structure model of adjacent mining in the mining area is constructed. The research results can provide technical support or theoretical reference for mining damage monitoring, subsidence control, and prediction in western mines.

Accurate information on the spatial and temporal distribution of abandoned cropland (AC) is crucial for protecting arable land, and maintaining regional food security and ecological stability. Nevertheless, the unavailability of dedicated monitoring for AC, along with the extended time frame required for remote sensing surveillance and the intricate transformation of land cover types following abandonment, poses considerable challenges in producing accurate AC maps for scientific purposes. To address these challenges, a new framework for AC identification was proposed. Specifically, the change detection was performed using the phenology-enhanced change vector analysis (PECVA) method, followed by a co-training semi-supervised classification method based on historical samples and sparse samples (HS-SSC) to classify the land cover type in change areas and obtain long-term land cover mapping results. Next, a land cover type change detector method was applied to identify the location and time of AC occurrences. Sufficient comparative experiments and accuracy validation were conducted to confirm the effectiveness of the PECVA and HS-SSC. The proposed method was verified in areas with single and double cropping per year, with average precision rates of 85.40% and 83.57% respectively for multi-year AC identification. This study offers a promising tool for identifying AC, which can aid in AC recultivation and serve for arable land conservation and land resource management. Our code is available at https://github.com/zhangtingting114/ACI.

Accurately extracting large-scale apple orchards from remote sensing imagery is of importance for orchard management. Most studies lack large-scale, high-resolution apple orchard maps due to sparse orchard distribution and similar crops, making mapping difficult. Using phenological information and multi-temporal feature-selected imagery, this paper proposed a large-scale apple orchard mapping method based on the AOCF-SegNet model. First, to distinguish apples from other crops, phenological information was used to divide time periods and select optimal phases for each spectral feature, thereby obtaining spectral features integrating phenological and temporal information. Second, semantic segmentation models (FCN-8s, SegNet, U-Net) were com-pared, and SegNet was chosen as the base model for apple orchard identification. Finally, to address the issue of the low proportion of apple orchards in remote sensing images, a Convolutional Block Attention Module (CBAM) and Focal Loss function were integrated into the SegNet model, followed by hyperparameter optimization, resulting in AOCF-SegNet. The results from mapping the Yantai apple orchards indicate that AOCF-SegNet achieved strong segmentation performance, with an overall accuracy of 89.34%. Compared to the SegNet, U-Net, and FCN-8s models, AOCF-SegNet achieved an improvement in overall accuracy by 3%, 6.1%, and 9.6%, respectively. The predicted orchard area exhibited an approximate area consistency of 71.97% with the official statistics.