Bibliometric analysis based on the Science Citation Index Expanded published by Thomson Scientific was carried out to identify the research status and future trends of remote sensing (RS) during 2010–2015. The analysis revealed the institutional, national, spatio-temporal, and categorical patterns in remote sensing research both from the WP (whole publications) viewpoint and the HCP (highly-cited publications) viewpoint. Statistical analysis results showed that remote sensing research almost doubled during 2010–2015. Environmental sciences comprised the most attractive subject category among remote sensing research. The International Journal of Remote Sensing was the most productive journal, and Remote Sensing of Environment published the most HCP among the 31 distributed journals. The productive ranking of countries was led by the U.S. both from the WP viewpoint and the HCP viewpoint, and CAS (Chinese Academy of Sciences) was the most productive institute both from the WP viewpoint and the HCP viewpoint with lower CPP (average number of citations per paper). Keyword analysis illustrated that model and algorithm research were the key points in RS during 2010–2015. RS data including Moderate-Resolution Imaging Spectroradiometer (MODIS), Landsat, synthetic aperture radar (SAR), and LiDAR (light detection and ranging) were the most frequently adopted, but the data usage of UAVs (unmanned aerial vehicles) and small satellites will be promoted in the future. With the development of data acquisition abilities, big data issues will become the challenges and hotspots of RS research, and new algorithms will continue to emerge.

Social media contains a lot of geographic information and has been one of the more important data sources for hazard mitigation. Compared with the traditional means of disaster-related geographic information collection methods, social media has the characteristics of real-time information provision and low cost. Due to the development of big data mining technologies, it is now easier to extract useful disaster-related geographic information from social media big data. Additionally, many researchers have used related technology to study social media for disaster mitigation. However, few researchers have considered the extraction of public emotions (especially fine-grained emotions) as an attribute of disaster-related geographic information to aid in disaster mitigation. Combined with the powerful spatio-temporal analysis capabilities of geographical information systems (GISs), the public emotional information contained in social media could help us to understand disasters in more detail than can be obtained from traditional methods. However, the social media data is quite complex and fragmented, both in terms of format and semantics, especially for Chinese social media. Therefore, a more efficient algorithm is needed. In this paper, we consider the earthquake that happened in Ya’an, China in 2013 as a case study and introduce the deep learning method to extract fine-grained public emotional information from Chinese social media big data to assist in disaster analysis. By combining this with other geographic information data (such population density distribution data, POI (point of interest) data, etc.), we can further assist in the assessment of affected populations, explore emotional movement law, and optimize disaster mitigation strategies.

The impact of insect pests on the yield and quality of rice is extremely significant, and accurate detection of insect pests is of crucial significance to safeguard rice production. However, traditional manual inspection methods are inefficient and subjective, while existing machine learning-based approaches still suffer from limited generalization and suboptimal accuracy. To address these challenges, this study proposes an improved rice pest detection model, MTD-YOLO, based on the YOLOv8 framework. First, the original backbone is replaced with MobileNetV3, which leverages optimized depthwise separable convolutions and the Hard-Swish activation function through neural architecture search, effectively reducing parameters while maintaining multiscale feature extraction capabilities. Second, a Cross Stage Partial module with Triplet Attention (C2f-T) module incorporating Triplet Attention is introduced to enhance the model’s focus on infested regions via a channel-patial dual-attention mechanism. In addition, a Dynamic Head (DyHead) is introduced to adaptively focus on pest morphological features using the scale–space–task triple-attention mechanism. The experiments were conducted using two datasets, Rice Pest1 and Rice Pest2. On Rice Pest1, the model achieved a precision of 92.5%, recall of 90.1%, mAP@0.5 of 90.0%, and mAP@[0.5:0.95] of 67.8%. On Rice Pest2, these metrics improved to 95.6%, 92.8%, 96.6%, and 82.5%, respectively. The experimental results demonstrate the high accuracy and efficiency of the model in the rice pest detection task, providing strong support for practical applications.

To address false detections in small object detection within remote sensing imagery caused by complex backgrounds and minute target sizes, we propose an enhanced YOLOv8s detection algorithm, named AL-YOLOv8. The detection head is designed based on Adaptive Spatial Feature Fusion (ASFF) to resolve issues where shallow-level detail features of small remote sensing targets are easily disrupted by backgrounds, while deep-level semantic features lack sufficient representation. We embed Large-Kernel Separate Attention (LSKA) in the deep feature layer to expand the receptive field and enhance the response intensity of small target features. Additionally, an IFIoU loss function is introduced by combining the dynamic attention mechanism from FocalerIoU with InnerIoU, mitigating regression bias for small target bounding boxes and improving small target localization accuracy. On the DIOR, RSOD, and NWPU VHR-10 datasets, the AL-YOLOv8 model achieves precision rates of 91.5%, 94.2%, and 91.8%, respectively, with mAP@0.5 scores of 89.8%, 96.9%, and 92.2%. These results demonstrate consistent improvements over YOLOv8s and show that AL-YOLOv8 effectively reduces false detections and enhances detection accuracy for small object detection in remote sensing applications.

Large-Scale land cover mapping (LLCM) based on deep learning models necessitates a substantial number of high-precision sample datasets. However, the limited availability of such datasets poses challenges in regularly updating land cover products. A commonly referenced method involves utilizing prior products (PPs) as labels to achieve up-to-date land cover mapping. Nonetheless, the accuracy of PPs at the regional level remains uncertain, and the Remote Sensing Image (RSI) corresponding to the product is not publicly accessible. Consequently, the sample dataset constructed through geographic location matching may lack precision. Errors in such datasets are not only due to inherent product discrepancies, and can also arise from temporal and scale disparities between the RSI and PPs. In order to solve the above problems, this paper proposes an LLCM framework for generating labels for use with PPs. The framework consists of three main parts. First, initial generation of labels, in which the collected PPs are integrated based on D-S evidence theory and initial labels are obtained using the generated trust map. Second, for dynamic label correction, a two-stage training method based on initial labels is adopted. The correction model is pretrained in the first stage, then the confidence probability (CP) correction module of the dynamic threshold value and NDVI correction module are introduced in the second stage. The initial labels are iteratively corrected while the model is trained using the joint correction loss, with the corrected labels obtained after training. Finally, the classification model is trained using the corrected labels. Using the proposed land cover mapping framework, this study used PPs to produce a 10 m spatial resolution land cover map of Cambodia in 2020. The overall accuracy of the land cover map was 91.68% and the Kappa value was 0.8808. Based on these results, the proposed mapping framework can effectively use PPs to update medium-resolution large-scale land cover datasets, and provides a powerful solution for label acquisition in LLCM projects.

High-resolution remote sensing images (HRSIs) are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies. However, their significant scale changes and wealth of spatial details pose challenges for semantic segmentation. While convolutional neural networks (CNNs) excel at capturing local features, they are limited in modeling long-range dependencies. Conversely, transformers utilize multihead self-attention to integrate global context effectively, but this approach often incurs a high computational cost. This paper proposes a global-local multiscale context network (GLMCNet) to extract both global and local multiscale contextual information from HRSIs. A detail-enhanced filtering module (DEFM) is proposed at the end of the encoder to refine the encoder outputs further, thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information. In addition, a global-local multiscale transformer block (GLMTB) is proposed in the decoding stage to enable the modeling of rich multiscale global and local information. We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively. Finally, we propose the semantic awareness enhancement module (SAEM), which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention. Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method. Specifically, our method achieved a mean Intersection over Union (mIoU) of 86.89% on the ISPRS Potsdam dataset and 84.34% on the ISPRS Vaihingen dataset, outperforming existing models such as ABCNet and BANet.