55 results about "Landslide detection" patented technology

The invention discloses a lightweight-based satellite landslide image intelligent detection method, device and equipment and a medium, and relates to the technical field of disaster detection, and the method comprises the steps: obtaining a whole-scene optical satellite image containing a landslide and a non-landslide region and landform auxiliary data; a dynamic segmentation strategy is adopted to carry out differential segmentation and standardized preprocessing on an image based on topographic data, and a standardized image is obtained. A target landslide image is screened through a double-layer machine learning model, the target image is input into an improved lightweight convolutional neural network for processing, an initial detection result is obtained, finally edge optimization and coordinate calibration are performed on the result, and an accurate landslide area detection result is output. The identification precision of the landslide image is effectively improved through dynamic segmentation and double-layer screening, the improved lightweight convolutional neural network realizes efficient detection in a low-resource environment, and the accuracy of a landslide detection result is further improved.

The invention discloses a landslide disaster detection method based on an improved remote sensing landslide image segmentation model, and relates to the technical field of remote sensing image processing. Comprising the following steps: S1, constructing a multi-feature input data set based on gradient tensor data; s2, preprocessing the multi-feature input data set and generating an enhanced feature data set through a feature enhancement technology; s3, constructing a landslide detection model; s4, optimizing the landslide detection model by combining the training data set, the cross entropy loss and an Adam optimizer to generate an optimized landslide detection model; s5, correcting the optimized landslide detection model based on a segmentation evaluation result to generate a final landslide detection model; and S6, generating a current landslide disaster detection result based on the final landslide detection model and the acquired landslide image real-time data. Implicit feature information of remote sensing image data can be fully mined, semantic segmentation precision of a traditional model is effectively improved, and landslide extraction precision and accuracy are improved.

The invention discloses a method for detecting landslides on both sides of a reservoir based on global-local feature perception collaboration. The method comprises the following steps: acquiring landslide images by adopting air-water collaboration and constructing a landslide detection data set on both sides of the reservoir; extracting and fusing the overall landslide terrain trend, crack details and multi-scale edge textures step by step through a four-level layered structure of the constructed landslide feature extraction network, and outputting high-robustness landslide features; and finally, the detection head outputs a bounding box and a confidence coefficient by using the landslide features, and performs landslide detection and deformation judgment in combination with a multi-period data intersection-union ratio. For landslide scenes on both sides of a reservoir, the overall landslide terrain trend and crack details are cooperatively perceived, and global-local feature complementation is realized. The detection head locates a landslide area through a classification and regression network, quantifies the existence probability, completes landslide detection and deformation determination of two banks of the reservoir, achieves accurate monitoring of an existing landslide body in a complex reservoir area environment, improves the reliability and timeliness of deformation identification, and provides a guarantee for safe operation of the reservoir.

Disclosed are systems and methods that employ elastic fiber optic, time-of-flight sensors for long distance landslide monitoring with sub-mm precision. The time-of-flight (ToF) sensor is integrated in conjunction with a single-photon avalanche diode (SPAD). By coupling both the emission source and the detector with a stretchable optical fiber, our inventive systems and methods continuously monitor the length of the stretchable optical fiber by measuring a traveling time of an optical pulse traversing the stretchable optical fiber. A significant, detectable change in the length of the stretchable optical fiber – indicative of ground movement or deformation, triggers an alarm, providing an early warning for potential landslides. As such, systems and methods according to aspects of the present disclosure provide a reliable, sensitive, precise, cost-effective, real-time solution for landslide detection and monitoring – a problem that has plagued the art.

The invention discloses a landslide detection deep learning method fusing geometry, context and frequency domain multi-source features, and belongs to the technical field of geological disaster recognition and landslide remote sensing information processing. The problem that a traditional landslide detection method in the prior art is low in recognition precision is solved. The method comprises the following steps: constructing a landslide detection model based on a GCF-Net architecture, and inputting a to-be-detected image into the landslide detection model; capturing feature information of different scales in the to-be-detected image through a ghost pyramid feature fusion module; an output characteristic pattern is input to a decoder through a GCFmodule; and an EdgeECI module in the decoder is used to process the output feature map of the GCFI module, and a final output feature map after fusion is obtained. The landslide detection accuracy is effectively improved, and the method can be applied to geological disaster monitoring and early warning, mountain traffic and infrastructure safety, water conservancy and energy engineering operation monitoring, remote sensing big data processing platforms and other scenes.

This invention relates to the field of landslide control technology, and discloses a real-time landslide monitoring method and system based on multi-sensor fusion and intelligent control. The method includes placing at least one sensor inside the metal reinforcement structure and at key locations on the landslide body to monitor landslide data in real time; acquiring real-time water composition imaging data of the slope under rainfall conditions; deriving rainfall volume from the landslide water composition imaging data; automatically adjusting the valve openings of drainage and diversion channels based on the rainfall volume; calculating the stability parameter distribution from the rainfall volume; obtaining a safety factor based on the stability parameter distribution; obtaining the instability probability of the slope through multiple grid cell safety factors; and optimizing the structural design of the metal reinforcement piles and reinforcement frame through finite element analysis. This invention improves the accuracy of landslide detection and early warning capabilities through real-time monitoring, data analysis, and automatic control of multi-source data.

The invention relates to a landslide detection method based on a high-resolution optical image, and the method comprises the steps: constructing an edge feature extraction module through employing a Canny filter, constructing the Canny filter and a convolution attention module, introducing the Canny filter and the convolution attention module into a U-Net model, constructing a landslide data set based on an optical remote sensing image, and carrying out the detection of the landslide. According to the method, training data is used, a Canny filter and a CBAM convolution attention module are used for cooperative training, verification data are sent to a belt detection model for detection, ten-time cross verification is carried out on the verification data, then averaging is carried out, and the Canny filter and the CBAM convolution attention module are introduced into a classical U-Net structure, so that the detection precision of landslide in a remote sensing image is remarkably improved. According to the method, the edge information in the image can be effectively extracted, key features of the landslide area can be automatically concerned, and the detection problem of a traditional method in a complex terrain and a boundary fuzzy area is solved.

The invention relates to the technical field of landslide detection, and discloses a semantic segmentation method, system and device for landslide remote sensing and a medium. The method comprises the following steps: acquiring a remote sensing image of a landslide, and extracting a feature map of the remote sensing image; performing feature extraction on the feature map by adopting a first attention mechanism to obtain a first-stage feature map focused on the landslide main body texture; performing feature extraction on the first-stage feature map by adopting a second attention mechanism to obtain a second-stage feature map focused on landslide edge extension; performing feature extraction on the second-stage feature map by adopting a third attention mechanism to obtain a third-stage feature map focused in the landslide slope body direction; performing feature extraction on the third-stage feature map by adopting a fourth attention mechanism to obtain a fourth-stage feature map focused on the landslide stripping trace; and performing semantic segmentation on the remote sensing image by combining the first-stage feature map, the second-stage feature map, the third-stage feature map and the fourth-stage feature map.

The application provides a semantic segmentation method for landslide detection by using medium-resolution multi-source remote sensing data, mainly including five steps: data selection and download, data preprocessing, model construction, model training and result evaluation. The application faces medium-resolution multi-source remote sensing data, proposes a new double-encoder semantic segmentation network with a self-attention mechanism, realizes feature expression and hierarchical fusion of multi-modal data, and further improves the landslide detection precision, thereby providing a technical reference for landslide detection in a large spatial scale.

The present application relates to the technical field of geological detection, and particularly relates to a landslide detection device, which comprises a shell, a friction induction surface on the inner wall of the shell, an electro-induction turntable connected to the shell in a relative rotating manner, an electro-induction boss on the circumferential side of the electro-induction turntable, a driving assembly comprising a driving member arranged outside the shell and a transmission member connected with the driving member and extending into the shell for driving the electro-induction turntable to rotate, and a circuit system arranged in the shell and electrically connected with the friction induction surface, wherein the circuit system is configured to capture an electric signal generated by the friction between the electro-induction boss and the friction induction surface when the driving member drives the transmission member to drive the electro-induction turntable to rotate under an external force, and transmit the electric signal to an external monitoring device. The present application converts the ground movement during a landslide into the rotation of the electro-induction turntable and the friction between the electro-induction boss and the friction induction surface to generate an electric signal, and realizes the detection of the landslide by capturing the electric signal.

The invention relates to the technical field of reasoning models, and discloses a model complementation-based target recognition method, which comprises a picture preprocessing step, a target recognition model preliminary recognition step, a confidence coefficient filtering step, a large model scene complementation detection step and a multi-model result fusion step. According to the method, consistent image input is provided through standardization processing, feature extraction deviation caused by size and color differences is reduced, meanwhile, the target recognition model more systematically captures landslide features through grid division and feature extraction, and missed detection of features with tiny expressions is reduced; secondly, low-confidence misjudgment can be filtered through confidence threshold screening, and different feature requirements can be met; furthermore, secondary detection of the large model supplements the complex scene recognition capability, and missing detection and misjudgment of a single model under low confidence or rare geological scenes are reduced; in addition, the weight fusion mechanism integrates the advantages of the target recognition model and the large model, and the overall accuracy and robustness of landslide detection are improved.

The application discloses a SAR image earthquake landslide boundary detection method and system based on deep learning, and the method comprises the following steps: inputting two SAR images before and after a landslide to generate a normalized mean ratio image, inputting the normalized mean ratio image into a pre-trained landslide detection network model based on deep learning after upsampling, extracting a landslide, extracting a boundary, and obtaining a landslide edge detection result; the landslide detection network model comprises an encoder and a decoder, the encoder comprises a backbone network and a coordinate attention empty space pyramid module, and the decoder comprises an attention adjustment module and a multi-scale feature fusion resampling module.The application can realize rapid, high-precision and automatic detection of large-scale earthquake landslide boundaries, and can realize detection of different earthquake types and different sizes of earthquake landslides.

This invention belongs to the field of ground-to-air transient electromagnetic measurement technology using magnetic sources, and more particularly relates to a semi-aerial transient electromagnetic data real-time transmission method suitable for landslide detection. This method utilizes an unmanned aerial vehicle (UAV) platform for rapid flight, automatically forming and superimposing tracks in mid-air, and obtaining kilobyte-level measured data. This data is then transmitted back to a ground receiving station in real time via wireless communication, facilitating real-time data processing. This method can provide faster interpretation results than conventional methods during landslide geological disaster surveys, providing a basis for disaster management in emergency landslide situations.

The application relates to the technical field of landslide detection, and discloses a semantic segmentation method, system, device and medium for mountain landslide remote sensing. The method comprises the following steps: acquiring a remote sensing image of a mountain landslide, and extracting a feature map of the remote sensing image; adopting a first attention mechanism to perform feature extraction on the feature map, so as to obtain a first-stage feature map focusing on the main texture of the mountain landslide; adopting a second attention mechanism to perform feature extraction on the first-stage feature map, so as to obtain a second-stage feature map focusing on the edge extension of the mountain landslide; adopting a third attention mechanism to perform feature extraction on the second-stage feature map, so as to obtain a third-stage feature map focusing on the direction of the mountain landslide slope body; adopting a fourth attention mechanism to perform feature extraction on the third-stage feature map, so as to obtain a fourth-stage feature map focusing on the stripping trace of the mountain landslide; and combining the first-stage feature map, the second-stage feature map, the third-stage feature map and the fourth-stage feature map to perform semantic segmentation on the remote sensing image.

The invention discloses a landslide displacement measurement method and system, and relates to the technical field of landslide detection, and the method comprises the steps: burying a detection inductor on a landslide body, connecting the two ends of the detection inductor to the top of the landslide body through insulated wires, connecting a voltmeter, collecting monitoring data, and carrying out the preprocessing; measuring the magnetic field intensity and the magnetic induction line direction on the landslide mass, calculating the included angle between the magnetic induction line and the detection inductor, constructing a correction model, calculating correction parameters, and obtaining displacement parameters; the monitoring voltage of the voltmeter is recorded, the actual displacement of the detection inductor in the magnetic induction line is calculated, and the landslide displacement is calculated and judged by combining displacement parameters. According to the invention, the detection is accurate, the external environment interference is small, the applied detection equipment is less, the possibility of landslide slippage can be accurately analyzed, the detection of landslide displacement at different depths can be met, and the possibility of damage to the detection equipment is reduced.

The invention provides a landslide area extraction method and device, electronic equipment and a storage medium, and belongs to the technical field of image data processing. The method comprises the following steps: respectively inputting a first remote sensing image of a time phase before occurrence of a landslide, a second remote sensing image of a time phase after occurrence of the landslide and a slope map into a first feature extraction network, a second feature extraction network and a third feature extraction network of a landslide extraction depth model, a first feature map, a second feature map and a third feature map which are correspondingly output are obtained; inputting the first feature map, the second feature map and the third feature map into a feature fusion module of a landslide extraction depth model to obtain a fused feature map output by the feature fusion module; and inputting the fused feature map into a decoder of the landslide extraction depth model to obtain a landslide extraction result output by the decoder. According to the method, the accuracy, robustness and efficiency of landslide automatic detection and mapping are improved, the slope map is introduced as a supplement of topographic information, and the identification capability of the model for the potential landslide area is enhanced.

The invention discloses a landslide mass detection method based on an improved distributed scatterer InSAR, and aims to accurately evaluate the movement speed of a landslide. According to the method, a regularization means is integrated in a covariance matrix estimation link, so that the phase error is effectively reduced, and the signal-to-noise ratio of a displacement time sequence is enhanced. Whether a synthetic data test or actual application data verification is passed, the technology shows that the precision and efficiency of landslide speed detection are remarkably improved while the coverage range of reliable monitoring points is expanded. The method is of great significance to accurate monitoring of landslide activities around the power line, and can effectively prevent potential threats of landslide to power transmission facilities.

The present application relates to the technical field of remote sensing image, and particularly relates to a highland mountainous area landslide detection method based on an improved YOLOv11 deep learning model, comprising: obtaining remote sensing images of a target area and performing pretreatment; constructing a YOLOv11 target detection model, and introducing a spatial adaptive feature modulation module and a convolution channel mixer into the model, the former is used for adaptively recalibrating the spatial dimension of a feature map to enhance the landslide area feature representation, and the latter is used for extracting local context information; using an improved point distance-based intersection over union loss function as a positioning loss, and training the model in combination with a multi-scale training strategy; inputting the image to be detected into the trained model, and outputting a landslide detection result. The cascade design of long-range feature modeling and local feature extraction enhances the feature extraction capability of the model for landslide targets, the loss function improves the boundary box regression accuracy, and effectively improves the accuracy and robustness of landslide detection in the complex background of highland mountainous areas.

This invention discloses a method and device for geological disaster prevention and control of power transmission and transformation tower foundations, belonging to the field of landslide detection technology. The method involves: real-time acquisition of optical remote sensing images, radar remote sensing images, microwave remote sensing images, and thermal infrared remote sensing images; overlaying optical and radar remote sensing images to generate slope topographic spatial distribution data; analyzing vegetation growth status in the optical remote sensing images to obtain vegetation spatial distribution data; overlaying the slope topographic spatial distribution data and vegetation spatial distribution data to obtain topographic-vegetation coupled spatial distribution data, thereby determining the landslide sensitivity level of each preset grid unit and generating landslide sensitivity level spatial distribution data; and retrieving water storage spatial distribution data based on microwave and thermal infrared remote sensing images to assess the stability of the tower foundation area where the power transmission and transformation equipment is located, obtaining landslide level distribution data to determine control measures. This invention solves the problem of low accuracy in landslide detection in existing technologies.

The present invention discloses a landslide detection method and system based on multi-scale feature fusion, relating to the field of remote sensing. The method comprises: acquiring a remote sensing image of a target area; inputting the remote sensing image into a trained landslide detection model; extracting bottom-level, mid-level, and high-level semantic features in the encoder portion of the model based on a local geometry module, a residual learning module, and a global correlation module; and outputting a landslide segmentation result after fusing the different-scale features extracted in the encoder portion based on the local geometry module and the global correlation module. The local geometry module and the residual learning module are constructed based on a convolutional neural network, and the global correlation module is constructed based on a Transformer network. The landslide detection model is obtained by training remote sensing images labeled with landslides. This method not only fully extracts multi-level features but also effectively fuses multiple features, avoiding computational redundancy and gradient vanishing problems, thereby improving the accuracy of the landslide detection model.

The application discloses a remote sensing image landslide detection method and system, and belongs to the technical field of landslide detection. The method adopts a data enhancement method to expand a landslide data set, and solves the problem of a small number of landslide samples in a training set. DarkNet53 is used to replace a commonly used backbone feature extraction network of FasterR-CNN, so that the problem of gradient disappearance caused by a too deep network is avoided, and the detection precision is improved.

The invention discloses a coherence change earthquake landslide detection method and device based on intensity difference enhancement. The method comprises the following steps: acquiring satellite data covering a to-be-detected area range; coherence information is extracted from the satellite data; determining a post-earthquake coherence map and an earthquake coherence map according to the coherence information; generating a post-earthquake coherence increase graph according to a difference value between the post-earthquake coherence graph and the earthquake coherence graph; generating a backscattering intensity difference chart according to the difference value of the backscattering coefficient chart after the earthquake and the backscattering coefficient chart before the earthquake; fusing the post-earthquake coherence enhancement image and the backscattering intensity difference image to obtain a fusion image of backscattering intensity difference enhancement and coherence change information; and performing seismic landslide detection on the to-be-detected area according to the fused image. According to the method, landslide detection is carried out by analyzing the backscattering intensity difference graph and the coherence change graph before and after the earthquake and integrating the advantages of the two graph characteristics in space structure and energy change, so that the precision and stability of earthquake landslide detection are improved.

The present disclosure provides a landslide detection method, device, program product and electronic equipment, and relates to the technical field of computers. The method comprises the following steps: determining a to-be-detected remote sensing image, then performing feature extraction on the to-be-detected remote sensing image through a backbone network of a landslide detection model to obtain a plurality of initial feature maps with different sizes; performing fusion processing on the plurality of initial feature maps with different sizes through a neck network of the landslide detection model to obtain a plurality of fused feature maps with different sizes; performing feature processing on the plurality of fused feature maps with different sizes through a receptive field attention submodule of the landslide detection model to obtain target receptive field features; and performing detection processing on the target receptive field features through a detection network of the landslide detection model to obtain a landslide detection frame and a landslide category label, thereby improving the accuracy of detecting landslides in remote sensing images to a certain extent.

The invention relates to the technical field of landslide detection, in particular to a high-position hidden landslide blind area intelligent monitoring system based on the Internet of Things. Comprising the following steps: S1, accessing multi-source heterogeneous time series data from an Internet of Things monitoring network arranged on a landslide mass, the multi-source heterogeneous time series data comprising deep displacement data, ground surface displacement data, pore water pressure data and rainfall data; s2, preset sensitive characteristic parameters are extracted from the multi-source heterogeneous time sequence data respectively, all the sensitive characteristic parameters are aligned to the same time reference in a unified mode, a multi-dimensional characteristic vector is obtained, and the sensitive characteristic parameters comprise displacement acceleration and osmotic pressure change rate; s3, performing correlation analysis according to the change trend of the displacement acceleration and the osmotic pressure change rate, and calculating a current abnormal index according to a correlation analysis result; s4, comparing the abnormal index with a preset threshold value defined based on a multi-parameter joint judgment rule; and when the combined judgment rule is met, judging that the landslide mass enters an abnormal state, and generating a corresponding early warning signal.

The present application discloses a landslide detection method, apparatus, device and storage medium, which relate to the field of image processing technology. The present application obtains a current frame image of a roadside slope area by real-time shooting; based on the current frame image, detects through a preset target classification model to obtain potential landslide targets, and achieves preliminary landslide target classification; tracks the potential landslide targets through a preset target tracking model to obtain the motion trajectory of the potential landslide targets; based on the motion trajectory, determines whether there are signs of landslide in the roadside slope area, thereby timely discovering signs of landslide and solving the problem of low reliability of landslide detection.

The application provides a kind of high-resolution image landslide detection method of combined spectrum, digital elevation model and auxiliary feature dual-branch network, including obtaining the high-resolution remote sensing image to be detected, digital elevation model and landslide true label;Extract auxiliary features, mine multiple characteristic factors from them and merge all images in channel;The image after merging is preprocessed, and landslide sample is generated;Different data enhancement means are used to increase the number and diversity of landslide data set;Residual and channel attention are added to the network model based on U-Net+++ to improve the network model;The image sample pair is respectively input into the improved U-Net+++ branch network, and the prediction result is obtained;The consistency of sample pair is mined and the model is optimized using the loss function;Based on the test sample, the model accuracy is evaluated and the landslide detection result map is output.The application provides a novel and effective landslide detection method, which can fully mine the invariance features of high-resolution landslide images and effectively improve the recognition accuracy of landslide.

The application provides a landslide detection method, device, equipment and medium to solve the problem that the prior art cannot accurately detect landslides. In the embodiment of the application, if the electronic device determines that the current frame image of the mountain collected changes relative to the last frame image, the motion state corresponding to each feature point in the current frame image is obtained, it is judged whether the speed in the motion state is greater than the preset speed threshold, if it is greater than the preset speed threshold, it is determined that there is a risk of landslide, and an alarm is given, so that the mountain can be dynamically detected, and it can be accurately determined whether there is a risk of landslide, improving the accuracy of landslide detection.

The present application relates to the field of image detection and segmentation, and particularly relates to a high-resolution image landslide detection and segmentation method. The present application establishes a landslide detection and segmentation network model based on optical images, and uses a transfer learning method to enable the model to detect and segment old landslides with less optical images, thereby quickly completing the detection and segmentation of landslides, determining the new and old types and specific distribution of landslides, improving the detection efficiency, and reducing the cost.

The invention discloses an interferometric synthetic aperture radar landslide detection and model construction method and device, and the method comprises the steps: carrying out the processing of multi-stage satellite data of a research region based on a small baseline set interferometric synthetic aperture radar technology, and obtaining an earth surface deformation rate InSAR image of the research region; constructing a training sample set for landslide detection by using InSAR images based on historical landslide distribution data of the research area and remote sensing images with the resolution higher than a preset resolution; taking a real-time detection model architecture as a target detection infrastructure, and constructing an anchor-frame-free end-to-end target detection backbone network; outputting a multi-scale enhanced feature map by using a small target enhanced pyramid SOEP module; fusing the enhanced feature map with P3, P4 and P5 features in the end-to-end target detection backbone network to obtain a fusion network; and training the fusion network by using the training sample set to obtain an interferometric synthetic aperture radar landslide detection model. According to the invention, landslide detection can be efficiently and accurately carried out.

The invention discloses mine landslide multi-view sound wave detection equipment and a mine landslide multi-view sound wave detection system, and relates to the technical field of mine landslide detection equipment. The mine landslide multi-view sound wave detection equipment comprises an outer sleeve, spiral plates are fixedly installed on the outer side of the outer sleeve, the ends, away from the outer sleeve, of the spiral plates are bent upwards, the spiral plates are evenly installed on the outer side of the outer sleeve in the axial direction, and separation rings are fixedly installed on the outer side of the outer sleeve. In cooperation with separation of a separation ring, when hole gaps are installed and filled and hole sealing treatment is carried out, backfilled soil is blocked in a segmented mode through the separation ring, meanwhile, the side, away from the outer sleeve, of the spiral plate is bent upwards, part of soil backfilled with the soil is fixed, and connection is formed between the outer sleeve and the inner wall of the hole through the gap; and the phenomenon that a gap exists between the outer sleeve and the inner wall of the hole, so that fluctuation is blocked due to the gap when the fluctuation is accepted, the acceptance of seismic source fluctuation is influenced, and detection data is influenced is prevented.