An earthquake-induced landslide (EQIL) is a rapidly changing process occurring at the Earth’s surface that is strongly controlled by the earthquake in question and predisposing conditions. Predicting locations prone to EQILs on a large scale is significant for managing rescue operations and disaster mitigation. We propose a deep learning framework while considering the source area feature of EQIL to model the complex relationship and enhance spatial prediction accuracy. Initially, we used high-resolution remote sensing images and a digital elevation model (DEM) to extract the source area of an EQIL. Then, 14 controlling factors were input to a stacked autoencoder (SAE) to search for robust features by sparse optimization, and the classifier took advantage of high-level abstract features to identify the EQIL spatially. Finally, the EQIL inventory collected from the Wenchuan earthquake was used to validate the proposed model. The results show that the proposed method significantly outperformed conventional methods, achieving an overall accuracy (OA) of 91.88%, while logistic regression (LR), support vector machine (SVM), and random forest (RF) achieved 80.75%, 82.22%, and 84.16%, respectively. Meanwhile, this study reveals that shallow machine learning models only take advantage of significant factors for EQIL prediction, but deep learning models can extract more effective information related to EQIL distribution from low-value density data, which is why its prediction accuracy is growing with increasing input factors. There is hope that new knowledge of EQILs can be represented by high-level abstract features extracted by hidden layers of the deep learning model, which are typically acquired by statistical methods.

The lack of high-resolution training sets for intelligent landslide recognition using high-resolution remote sensing images is a major challenge. To address this issue, this paper proposes a method for reconstructing low-resolution landslide remote sensing images based on a Super-Resolution Generative Adversarial Network (SRGAN) to fully utilize low-resolution images in the process of constructing high-resolution landslide training sets. First, this paper introduces a novel Enhanced Depth Residual Block called EDCA, which delivers stable performance compared to other models while only slightly increasing model parameters. Secondly, it incorporates coordinated attention and redesigns the feature extraction module of the network, thus boosting the learning ability of image features and the expression of high-frequency information. Finally, a residual stacking-based landslide remote sensing image reconstruction strategy was proposed using EDCA residual blocks. This strategy employs residual learning to enhance the reconstruction performance of landslide images and introduces LPIPS for evaluating the test images. The experiment was conducted using landslide data collected by drones in the field. The results show that compared with traditional interpolation algorithms and classic deep learning reconstruction algorithms, this approach performs better in terms of SSIM, PSNR, and LPIPS. Moreover, the network can effectively handle complex features in landslide scenes, which is beneficial for subsequent target recognition and disaster monitoring.

Ecological restoration is an important strategy for mitigating environmental degradation, and the effectiveness evaluation of ecological restoration is of profound significance for the scientific implementation of restoration projects. This study improved the Patch-generating Land Use Simulation (PLUS) model. It was used to simulate the land use patterns under multi-scenarios such as natural development (ND), economic priority (EP), and ecological restoration (ER) in 2030. An evaluation framework covering ecological “Restoration–Monitoring–Effectiveness” (RME) was proposed. Based on 30 m high-resolution remote-sensing data from 2000 to 2020, the land use distribution, landscape pattern changes, and ecosystem services under different scenarios were evaluated and predicted in the Yellow River Basin of Sichuan to verify the effectiveness of the evaluation framework. The results showed the following: (1) Under the ER scenario, the transfer of land use types in 2020–2030 was mainly characterized by an increase in the area of wetlands and a decrease in the area of built-up land. (2) There were obvious differences in land use and landscape patterns under different scenarios. Compared with the ND and EP scenarios, the growth of the construction rate was suppressed in the ER scenario, and the coverage of grassland and wetlands increased significantly. (3) The mean values of ecosystem services in the ER scenario were higher than those in the ND and EP scenarios. These findings clearly indicate that the RME evaluation system can accurately evaluate the ecological restoration effects under multi-scenarios in the future, providing a new perspective for ecological restoration evaluation in other regions.

In recent years, much attention has been paid to using deep learning techniques for land seismic first break (FB) picking. Among these, the deep learning-based 3-D FB picking method has shown stronger noise resistance than the 2-D method, as it simultaneously considers the correlation of FBs in both crossline and inline directions. In existing studies, whether using 2-D or 3-D methods, they are often regarded as a binary semantic segmentation problem of pre- and post-first break (PPFB). The FB time is determined by dividing the image with a binary classification mask. However, due to the instability of the decision threshold setting of the PPFB mask strategy, different thresholds can cause different FB picking results. Considering this, we propose a two channel (2C) mask strategy that utilizes the feature interaction between a strip-like mask and a line-like mask to predict the confidence of FBs in seismic traces, thus realizing the FB picking with uniqueness. In addition, to enhance the global feature acquisition capability of the network, we construct a 3-D FB picking network USwinNet based on Swin Transformer, which establishes a larger receptive field through a multi-stage self-attention mechanism. Experimental results demonstrate that our method significantly improves the picking accuracy compared to existing methods. It also exhibits strong generalization capabilities, making it more suitable for practical engineering under complex geological conditions.

China’s Ecological Protection Red Lines (ERLs) policy has proven effective in constructing regional ecological security patterns and protecting ecological space. However, the existing methods for the identification of high conservation value areas (HCVAs) usually use physical models, whose parameters and processes are complex and only for a single service, affecting the ERL delineation. In this study, the data-driven machine learning (ML) models were innovatively applied to construct a framework for ERL identification. First, the One-Class Support Vector Machine (OC-SVM) was used to generate negative samples from natural reserves and ecological factors. Second, the supervised ML models were applied to predict the HCVAs by using samples. Third, by applying the same ecological factors, the traditional physical models were used to assess the ecological services of the study area for reference and comparison. Take Chengdu–Chongqing Urban Agglomeration (CY) as a case study, wherein data from 11 factors and 1822 nature reserve samples were prepared for feasibility verification of the proposed framework. The results showed that the area under the receiver operating characteristic curve (AUC) of all ML models was more than 97%, and random forest (RF) achieved the best performance at 99.57%. Furthermore, the land cover had great contributions to the HCVAs prediction, which is consistent with the land use pattern of CY. High-value areas are distributed in the surrounding mountains of CY, with lush vegetation. All of the above results indicated that the proposed framework can accurately identify HCVAs, and that it is more suitable and simpler than the traditional physical model. It can help improve the effectiveness of ERL delimitation and promote the implementation of ERL policies.