In the fields of machine learning and deep learning, ensuring model robustness and reliability is critical. One major challenge is the handling of Out-of-Distribution (OOD) samples, the presence of In-Distribution (ID) noise in existing OOD datasets. It can increase the risk of misclassification and uncertainty in models. Therefore, we first proposed a data purification method driven by multi-model reasoning consistency. As far as we know, this is the first method to automatically purify data in the OOD field. At the same time, we used this method to create the OOD-R (Out-of-Distribution-Rectified) dataset. OOD-R leverages noise filtering methods to improve dataset quality, providing a more reliable benchmark for evaluating OOD detection algorithms. In addition, we propose ActFun, a method designed to improve the model’s response to different inputs, enhance the stability of feature extraction, and mitigate issues associated with model overconfidence. ActFun works by reducing the influence of specific hidden units, enabling the model to better estimate uncertainty in OOD detection and improve generalization ability. Experiments show that implementing ActFun remarkably improves performance on the OOD dataset.

Extracting polygonal building footprints from off-nadir imagery is crucial for diverse applications. Current deep-learning-based extraction approaches predominantly rely on semantic segmentation paradigms and postprocessing algorithms, limiting their boundary precision and applicability. However, existing polygonal extraction methodologies are inherently designed for near-nadir imagery and fail under the geometric complexities introduced by off-nadir viewing angles. To address these challenges, this article introduces the polygonal footprint network (PolyFootNet), a novel deep-learning framework that directly outputs polygonal building footprints without requiring external postprocessing steps. The PolyFootNet employs a high-quality mask prompter to generate precise roof masks, which guide polygonal vertex extraction in a unified model pipeline. A key contribution of the PolyFootNet is introducing the self-offset attention (SOFA) mechanism, grounded in Nadaraya–Watson regression, to effectively mitigate the accuracy discrepancy observed between low-rise and high-rise buildings. This approach allows low-rise building predictions to leverage angular corrections learned from high-rise building offsets, significantly enhancing overall extraction accuracy. Additionally, motivated by the inherent ambiguity of building footprint extraction (BFE) tasks, we systematically investigate alternative extraction paradigms and demonstrate that a combined approach of building masks and offsets achieves superior polygonal footprint results. Extensive experiments validate PolyFootNet’s effectiveness, illustrating its promising potential as a robust, generalizable, and precise polygonal BFE method from challenging off-nadir imagery. To facilitate further research, we will release pretrained weights of our offset prediction module athttps://github.com/likaiucas/PolyFootNet

With the continuous enhancement of remote sensing image resolution and the rapid advancement of deep learning techniques, land cover mapping is undergoing a significant transformation from pixel-level segmentation to object-based vector modeling. This shift imposes higher demands on deep learning models, requiring not only precise delineation of object boundaries but also the preservation of topological consistency among geographic elements. However, existing public datasets face three major limitations: limited class annotations, restricted data scale, and the lack of spatial structural information, which severely hinder the development of breakthrough methods in high-resolution remote sensing vectorization. To address these challenges, we present IRSAMap, the first global remote sensing dataset designed for large-scale, high-resolution, multi-feature land cover vector mapping. This dataset offers four key advantages: First, a comprehensive element vector annotation system that includes over 1.8 million instances of 10 typical natural and man-made objects, such as buildings, roads, rivers, and trees, employing a unified vector annotation standard framework that ensures both semantic integrity and spatial structural accuracy. Second, an intelligent annotation workflow incorporating “manual pre-annotation + AI-based training and inference + manual review and correction,” which enhances annotation efficiency while ensuring consistency. Third, a global coverage that spans 67 regions across six continents, representing diverse terrain types, including urban and rural areas, with a total coverage area exceeding 1,000 square kilometers. Fourth, multi-task adaptability, supporting various tasks such as pixel-level land cover classification, building outline regularization extraction, road centerline extraction, and panoramic segmentation. As a fundamental resource for remote sensing intelligent interpretation, IRSAMap provides a standardized benchmark for the paradigm shift from pixels to objects, which will significantly advance the development of high-precision geographic feature automation, collaborative modeling, and other cutting-edge research directions. The dataset is of great value for applications such as global geographic information updating and digital twin construction. IRSAMap is publicly available at https://github.com/ucas-dlg/IRSAMap.

Accurate cartographic-level polygonal vector extraction from remote sensing images is crucial for land survey and land cover mapping. However, current deep learning-based segmentation models often generate raster outputs with irregular boundaries, making them unsuitable for applications requiring precise vector data. This study presents the Prompt-based Cartographic-level Polygonal (PCP) Vector Extraction framework, which leverages segmentation results as prompts to guide the extraction process and enhance regularity. Built upon the Segment Anything Model (SAM), the PCP extracts the segmentation mask and an additional vertex map. To improve vertex extraction accuracy and enable more regular polygon generation, two key modules are introduced: the Iterative Upscaling Refinement (IUR) module, which addresses challenges related to low-resolution feature maps, and the Shape Rule-Based Vertex Filtering and Connecting (SRVFC) module, which enhances the vertex filtering process by learning from shape features. Experimental results on the Land Survey Vector (LSV), LoveDA and WHU-Mix (Vector) datasets demonstrate that the PCP outperforms existing methods in terms of vertex precision and recall, reflecting geometric accuracy, as well as in complexity-aware Intersection over Union (C-IoU), which balances overall accuracy and simplicity. Thus, the PCP framework provides a promising solution for cartographic-level vector extraction in remote sensing applications. The source code is available at https://github.com/wchh-2000/PCP.

More accurate extraction of invisible building footprints from very-high-resolution (VHR) aerial images relies on roof segmentation and roof-to-footprint offset extraction. Existing methods based on instance segmentation suffer from poor generalization when extended to large-scale data production and fail to achieve low-cost human interaction. This prompt paradigm inspires us to design a promptable framework for roof and offset extraction, and transforms end-to-end algorithms into promptable methods. Within this framework, we propose a novel offset-building model (OBM). Based on prompt prediction, we first discover a common pattern of predicting offsets and tailored Distance-NMS (DNMS) algorithms for offset optimization. To rigorously evaluate the algorithm’s capabilities, we introduce a prompt-based evaluation method, where our model reduces offset errors by 16.6% and improves roof Intersection over Union (IoU) by 10.8% compared to other models. Leveraging the common patterns in predicting offsets, DNMS algorithms enable models to further reduce offset vector loss (VL) by 6.5%. To further validate the generalization of models, we tested them using a newly proposed test set, Huizhou test set, with over 7,000 manually annotated instance samples. Our algorithms and dataset will be available athttps://github.com/likaiucas/OBM.

High-speed train (HST) seismology has emerged recently, which regards the HST as a repeatable moving seismic source. To make full use of HST source for detection and monitoring of near-surface geological structures and physical properties, the wavefield modeling for HST source is crucial to deeply understand and illustrate the wavefield characteristics of HST source. In this paper, we simplify the near surface as a two-layered model, and treat the HST as a moving combination source with the uniform velocity, containing a series of moving subsources (single wheel). Using the principle of superposition, we derive the semi-analytical formula of HST wavefield for a two-layered model. Through computing the synthetic seismograms, the multi-domain characteristics of HST wavefield are comprehensively analyzed. Furthermore, we illustrate the wavefield energy distribution of HSTs with the different moving velocities. When the moving velocity of HST source exceeds the Rayleigh-wave velocity, the response in the direction perpendicular to the railway exhibits the slower decay and higher amplitude. To integrate with the practical applications, we make a similarity comparison between the synthetic data and the field data. The results of the two-layered model show good agreement with the field data in both time and frequency domains. In particular, we evaluate the detection capability of HST vibration signals received at distances away from the railway up to a few kilometers. These conclusions will contribute to the practical applications of HST source in seismic imaging and monitoring of geological conditions around high-speed railway.

The semantic segmentation of high-resolution (HR) remote sensing images (RSIs) has been extensively researched in various applications. However, segmentation networks are prone to significant performance degradation on unlabeled data due to domain shift, such as data distribution shifts arising from distinct geographic locations. To address this issue, we propose a multilevel-guided curriculum domain adaptation (MuGCDA) approach for joint samplewise, categorywise, and pixelwise tasks, which facilitates the final fine-grained segmentation task by guiding the target domain to acquire samplewise and categorywise domain-robust properties. Concretely, at the sample level, we formulate a sample spatial relationship consistency guidance (SSCG) loss that guides the target domain to acquire similar sample spatial relationship properties to the source domain. At the category level, we propose a category layout structure consistency guidance (CLCG) module that guides the target domain to acquire consistent layout properties. At the pixel level, we design an adaptive hierarchical pseudolabel weight setting (AHPWS) method with a self-training (ST) paradigm to reduce the effect of label noise while improving the quality of the generated pseudolabels. Furthermore, to improve the stability of the training process, we use a momentum network (MN) as the teacher network to obtain the property knowledge and pseudolabels, and then guide the whole domain transfer process of the segmentation network, which acts as the student network. Extensive comparison and ablation experiments are conducted in several cross-space and cross-spectral scenes, and the results show that our method achieves significant performance improvements in cross-domain scenes for HR RSIs.

Detecting and segmenting unlearned objects in unknown environment is a very important visual perception ability to enhance industrial intelligence. In this article, we present a novel conditional random field model integrating unimodal and cross-modal terms for detecting and segmenting object instances without knowing their categories and without sampling extra proposals. This model takes a paired image and point cloud as input, from which we first develop a set of novel category-independent features to distinguish objects. Then, a set of unary, pairwise, and higher order potentials are designed according to these category-independent features, and the cross-modal potential is introduced as a novel global constraints to keep the spatial consistency in both 2-D and 3-D modalities. In this novel model, the unlearned object detection and segmentation is treated as the process of pixel labeling. Thus, adjacent or occlusion object instances can also be separated efficiently from a labeled map. By comparison with the baseline methods, experimental results on a public RGB+D dataset show that the proposed model can obtain better performance with improved precision and recall rate. Moreover, we use the proposed method in a real industrial scene and achieve satisfactory performance.

Landslides are a kind of geologic hazard triggered by anthropogenic or natural factors. Change detection is an important technique to extract the landslide area from pre- and postdisaster images. As landslides are similar in spectrum to bare land and it is difficult to absolutely calibrate the radiation of multitemporal images, the detection method may lead to significant errors or omissions. By modeling the relative relationship between adjacent pixels from multitemporal images, errors or omissions and illumination influences will be reduced during detection. With the aim of extracting landslides with an automatic and robust process, this letter proposes a practical method based on multitemporal data and spatiotemporal model. First, the normalized difference vegetation index (NDVI) and built-up area presence index (PanTex) features series were produced, which can reflect changes in vegetated and built-up areas, respectively. Then, we used a spatiotemporal context (STC) model to detect landslide from feature series. Finally, the landslide map could be derived. The proposed method was applied to detect landslide using GaoFen (GF) series satellite. The experimental results demonstrated the effectiveness and robustness of our method.

Motion objects detection becomes more and more important in the applications of video surveillance, e.g., intrusion detection. The spatiotemporal saliency is an effective feature to describe object motion. However, there is a lot of redundancy in the spatial information preventing to obtain accurate saliency in an effective way. At the same time, temporal information cannot be accurately described because it is affected by uneven brightness, complex background, and fast-moving objects, especially at the edge of moving objects. In this article, we develop a novel method to tackle these problems and obtain more accurate spatiotemporal saliency. The key idea is the superpixel merging based on our maximum consistency model in feature space, through which the redundant spatial information is decreased and inhibit some temporal information errors. Experimental evaluations on the NNT dataset and surveillance videos show that the proposed method achieves better performance by comparing with some state-of-the-art methods, and can effectively detect intrusion entities.

Landslides occur frequently and it is very meaningful to monitor them in disaster researches. Extracting the landslide from the high resolution satellite with fewer false changes is an important problem to be solved for remote sensing change detection research. In high spatial resolution and multi-temporal remote sensing images, landslides show significant differences with the background in both temporal and spatial neighborhoods, the knowledge of fusing temporal and spatial information is more favorable for landslide detection. NDVI (Normalized Difference Vegetable Index) and PanTex (built-up area presence index) features can reflect these changes in vegetable and built-up area respectively. Based on GF-1 CCD data and the change detection method of spatio-temporal context, the proposed method can detect the landslide area accurately by using NDVI and PanTex features.

The Separability and Thresholds (SEaTH) algorithm calculates the the SEparability and the corresponding THresholds of object classes for any number of given features. However, it is applicable only to the normally distributed training data. To cope with the problem, The Classification And Regression Tree (CART) coupled with SEaTH for object-based classification approach is proposed in the paper. The idea of this method is derived from the merits of the CART which can effectively analyze the non-normally distributed data and automatically create the classification tree. A comparison of classification results demonstrate that the solution for object-based classification proposed in this article can be used to obtain a higher classification accuracy than SEaTH classification.

Multi-scale spatial context which integrates spatial metrics and textural metrics is used to characterize land-use parcel and a hybrid land-use mapping approach is proposed in this paper. In terms of land-use characterization, the contributions of textural and spatial metrics are evaluated quantitatively. In terms of land-use categorization, a hybrid land-use classification scheme which combines Pairwise Decision Tree based Support Vector Machine (PDTSVM) and rule based decision tree is designed to classify parcels into construction, cultivated and uncultivated agricultural parcels. Experiment show that applying the presented technique can facilitate land-use mapping.

Since UAV oblique sequence images have large differences in perspective, it is difficult to achieve fully automatic, highly accurate registration using conventional image registration methods. To solve this problem, we present a sub-pixel image registration method based on ASIFT. Position of match points is corrected using the weighted least squares algorithm affine model (WLSM) one by one. Characteristics are selected and initial parameters estimation is also done through adaptive maximum cross-correlation algorithm (NCC). Experiments show that the proposed method is superior to conventional methods, such as SIFT and ASIFT, and this Method can achieve sub-pixel level registration precision.

Road traffic volume monitoring plays an important role in transportation planning and spatial development, particularly in urban areas. The high-resolution satellite imagery provides a new data source to detect vehicles. Meanwhile, Satellite image covers large areas instantaneously, providing a possibility for snapshotting road traffic conditions. In this paper, we proposed an approach based on watershed image segmentation to detect the urban road vehicles from GF-2 imagery. The vehicles detection involves the two main steps: Firstly, a GIS road vector map and vegetation masks were applied to the image to guide vehicle detection by restricting the roads only. Secondly, watershed image segmentation was performed to separate bright and dark vehicles from the background in the road region. Then, a rule-based classifier was established to classify the image objects into the vehicle and the non-vehicle objects by using the spectral and shape feature information of image objects. Finally, the overall performance of the vehicle detection were compared with the manually counts, yielding overall accuracy of 81% with 93% classification accuracy. This detection accuracy may be considered acceptable for operational use in traffic monitoring.

Compared with the traditional aerospace remote sensing platforms, The UAV was a new remote sensing platform, with the advantages of flexible, easy operation, high resolution and low-cost, which has been successfully applied to various field of the national economy. The manual extraction of buildings is an extremely labor-intensive, time-consuming work. So the automatic extraction of buildings elevation information has become the focus of international research. This paper take the UAV DSM data as the experimental data of the building's elevation information extraction, adopt the method of regularization DSM data, firstly, based on the multi-scale morphological filtering method, Separate building elevation information from terrain elements, get the preliminary building elevation information. Then use the small region removal method combined morphological filtering to remove the small noise, get final building elevation information, Experiments show that this method can get a better result.

A new cloud detection method based on color model and undecimated wavelet transform was proposed for the basic properties of the cloud area on remote sensing images and band features. First of all, modified HSI Color model transformation was applied to original images combined with the band information in order to extract approximate boundary of cloud area. Secondly, as in binary sampling process of wavelet decomposition, there was losing spectral information and texture information problems. In order to solve this, we used undecimated wavelet transform and the normalized double threshold value method to determine the exact boundary of cloud area. At last, our results were assessed using False Recognition Rate and Accuracy. The assessment was compared with cloud detection methods based on wavelet transform and single threshold. The Result means that proposed algorithm has high detection precision.

This paper introduces an improved image registration method for infrared and visible remote sensing image based on NSCT and SIFT algorithm. As the infrared and visible spectral characteristics of remote sensing images are inconsistent, the gray difference between these two images are quite distinct, so there are less matching points by using SIFT algorithm directly. The method of NSCT can decompose the image to a series of frequency channels and high frequency channels contain the edge details of the original image. It could get more matching points and improve the matching rate and the accuracy of registration by using SIFT registration method between two high frequency channels of decomposed images.

Using remote sensing images to monitor land use information, extract changed information, which has a wide range of research value and is the key technologies in the field of land use monitoring. The article analyses the current methods of change detection and designs a fast detection method of changed blocks in land use map with the before and after remote sensing images. The method uses the spirit of object-oriented technology, and makes the polygons in the land use map directly as the objects for research. Then calculates the feature value(mean value of layer 1 and layer 2, NDVI, GLCM Homogeneity of layer 3 and layer 4 used in this article ) of each objects in the before and after remote sensing images. After normalized processing for feature value, calculates the Euclidean distance between feature vectors. Finally, extracts the changed polygons in land use map according to the threshold. The experiment proves that the article's method can fast and accurately detect the changed blocks in land use map.