After an attack the by pine wood nematode, pine tree needles turn red. Using convolutional neural networks (CNNs) based object detection methods, machines can detect red-attacked trees. However, most deep learning object detection algorithms (such as Faster R-CNN and YOLO among others) often require a large number of labelled training datasets, where in each image every object must be given a bounding box label. To increase the cost-effectiveness of this process, we propose a simple yet efficient weakly supervised processing pipeline, based on class activation maps to locate the target. Unlike object detection methods that require bounding-box-labelled data for training, the proposed pipeline only needs image-level-labelled data. Using the proposed pipeline, we could achieve an average precision (AP) of 91.82% on test dataset. Comparing with sliding window-based method which achieves an average precision (AP) of 89.95%, our method not only gets a better AP but also runs faster than sliding window-based pipeline. This result not only indicates that the pipeline is a highly effective one but also demonstrates that image-level-labelled aerial images can be used for the detection of red-attacked tree. The proposed method should also find use in other object detection applications in the field of remote sensing.

Typical visual simultaneous localization and mapping (SLAM) systems rely on front-end odometry for feature extraction and matching to establish the relations between adjacent images. In a low-light environment, the image obtained by a camera is dim and shows scarce information, hindering the extraction of sufficient stable feature points, consequently undermining visual SLAM. Most existing methods focus on low-light enhancement of a single image, neglecting the strong temporal correlation across images in visual SLAM. We propose a method that leverages the temporal information of an input image sequence to enhance the low-light image and employed the enhanced result to improve the feature extraction and matching quality of visual SLAM. Our method trains a three-dimensional convolutional neural network to estimate pixelwise grayscale transformation curves to obtain a low-light enhancement image. Then, the grayscale transformation curves are iteratively applied to obtain the final enhanced result. The training process of the network does not require any paired reference images. We also introduced a spatial consistency loss for the enhanced image to retain the content and texture of the original image. We further integrated our method into VINS-Mono and compared with similar low-light image enhancement methods on the TUM-VI public dataset. The proposed method provides a lower positioning error. The positioning root-mean-squared error of our method is 19.83% lower than that of Zero-DCE++ in low-light environments. Moreover, the proposed network achieves real-time operation, being suitable for integration into a SLAM system.

The large view angle and complex background of UAV images bring many difficulties to the detection of small pedestrian targets in images, which are easy to be detected incorrectly or missed. In addition, the object detection models based on deep learning are usually complex and the high computational resource consumption limits the application scenarios. For small pedestrian detection in UAV images, this paper proposes an improved YOLOv5 method to improve the detection ability of pedestrians by introducing a new small object feature detection layer in the feature fusion layer, and experiments show that the improved method can improve the average precision by 4.4%, which effectively improves the pedestrian detection effect. To address the problem of high computational resource consumption, the model is compressed using channel pruning technology to reduce the consumption of video memory and computing power in the inference process. Experiments show that the model can be compressed to 11.2 MB and the GFLOPs of the model are reduced by 11.9% compared with that before compression under the condition of constant inference accuracy, which is significant for the deployment and application of the model.

Geocoding converts unstructured geographic text into structured spatial data, which is crucial in fields such as urban planning, social media spatial analysis, and emergency response systems. Existing approaches predominantly model geocoding as a geographic grid classification task but struggle with the output space dimensionality explosion as the grid granularity increases. Furthermore, these methods generally overlook the inherent hierarchical structure of geographical texts and grids. In this paper, we propose a hierarchy-aware geocoding model based on cross-attention within the Seq2Seq framework, incorporating S2 geometry to model geocoding as a task for generating grid labels and predicting S2 tokens (labels of S2 grids) character-by-character. By incorporating a cross-attention mechanism into the decoder, the model dynamically perceives the address contexts at the hierarchical level that are most relevant to the current character prediction based on the input address text. Results show that the proposed model significantly outperforms previous approaches across multiple metrics, with a median and mean distance error of 41.46 m and 93.98 m, respectively. Furthermore, our method achieves superior results compared to others in regions with sparse data distribution, reducing the median and mean distance error by 16.27 m and 7.52 m, respectively, suggesting that our model has effectively mitigated the issue of insufficient learning in such regions.

Geocoding is a fundamental component of geographic information science that plays a crucial role in various geographical studies and applications involving text data. Current mainstream geocoding methods fall into two categories: geodesic-grid prediction and address matching. However, the geodesic-grid-prediction method’s localization accuracy is hindered by the density of grid partitioning, struggling to strike a balance between prediction accuracy and grid density. Address-matching methods mainly focus on the semantics of query text. However, they tend to ignore keyword information that can be used to distinguish candidates and introduce potential interference, which reduces matching accuracy. Inspired by the human map-usage process, we propose a two-stage address-matching approach that integrates geodesic-grid prediction and text-matching models. Initially, a multi-level text-classification model is used to generate a retrieval region proposal for an input query text. Subsequently, we search for the most relevant point of interest (POI) within the region-proposal area using a semantics-based text-retrieval model. We evaluated the proposed method using POI data from the Beijing Chaoyang District. The experimental results indicate that the proposed method provides high address-matching accuracy, increasing Recall@1 by 0.55 to 1.56 percentage points and MRR@5 by 0.54 to 1.68 percentage points.

Geospatial target localization refers to determining the geographic location of targets from natural language queries, which is essential for applications such as urban management, emergency response, and location-based services. As user queries evolve from address matching toward complex forms incorporating multiple spatial semantic factors, the localization process increasingly demands the coordination of heterogeneous tools and data sources. Automation remains hindered by the lack of a unified structured representation for semantic factors, the difficulty of translating complex spatial constraints into executable operations, and the unreliability of single-round planning and execution. This study proposes SGTAgent, an agent framework for geospatial target localization from complex spatial semantic queries. The parser transforms queries into structured spatial semantic factors, the planner organizes dependent spatial workflows, and the feedback mechanism distinguishes local parameter refinement from workflow restructuring. Experiments show that SGTAgent achieves higher localization accuracy than the evaluated generic baselines across multiple LLM backbones. Using DeepSeek-V4.1-Flash, SGTAgent achieves localization accuracies of 39.86%, 68.76%, and 81.88% at distance thresholds of 100, 300, and 500 m, respectively, outperforming the evaluated agents under the corresponding experimental configurations. On complex queries using GPT-4o, it achieves an LA@500m of 80.47%, compared with 70.12% for ReAct. Ablation experiments show that removing the complete Restate–Replan feedback mechanism reduces the 500 m localization accuracy from 81.88% to 74.83%, demonstrating the effectiveness of the execution feedback mechanism under the evaluated configuration. An independent evaluation of the parser achieves macro-F1 scores of 89.76% on the original benchmark and 89.62% on independently formulated queries, indicating consistent semantic factor extraction capability across different query formulations.