199 results about "Imaging interpretation" patented technology

The present invention relates to the technical field of intelligent remote-sensing-image interpretation, and specifically provides a texture-aware and boundary-aware target detection method and system for a weak and small target in a remote sensing image. The method comprises: inputting a remote sensing image into a feature extraction network to extract a basic feature, and then extracting a texture-aware feature; inputting the remote sensing image into a boundary map extraction module to extract a binary boundary map, and then extracting a boundary-aware feature; fusing the boundary-aware feature with the basic feature by means of a boundary-guided feature module, so as to obtain a boundary-guided feature; and decoupling the texture-aware feature and the boundary-guided feature and outputting same, inputting the texture-aware feature and the boundary-guided feature into a task-decoupling RCNN for double-branch decoupling prediction, so as to obtain a classification and positioning result, and completing texture-aware and boundary-aware target detection. By means of the method and system in the present invention, information that is hard for remote-sensing weak and small targets to show can be mined, thereby further improving the expression of the weak and small targets, and thus improving the detection performance regarding the weak and small targets.

The invention relates to the technical field of image processing, and provides an image artifact detection method and system based on a deep learning technology, and the method comprises the following steps: carrying out the preprocessing of an obtained to-be-processed image, and carrying out the feature extraction of the preprocessed image; position coding is carried out on the extracted feature map A, and the extracted feature map A is fused with the feature map A; performing multi-layer encoding and decoding on the fused feature map through an encoder and a decoder to obtain a feature map B containing an artifact target area; and generating an anchor frame of an artifact region for a target region in the feature map B, and dynamically adjusting the size and position of the anchor frame according to local information of the down-sampling rate, texture complexity and edge strength of the feature map B to obtain an artifact region detection result after the anchor frame is labeled. Automatic artifact detection and quantitative evaluation of the influence degree can be achieved, the accuracy and efficiency of medical image interpretation are improved, and more reliable data support is provided for clinical decision making.

The invention relates to a remote sensing image domain generalization semantic segmentation method based on multi-scale instance decoupling, which relates to the technical field of remote sensing image interpretation and comprises the steps of multi-scale instance coding, domain invariant instance decoupling and domain generalization semantic decoding. Image features in a segmented backbone network are used as input, and a multi-scale instance coding module effectively senses feature responses of cross-domain instances through deep convolution, multi-scale cooperation and multi-path sensing and is not affected by scale differences, spatial deformation and noise; designing a domain invariant instance decoupling module to decouple the cross-domain style from the image representation; and finally, the decoupled features are input into a domain generalization semantic decoding module so as to predict a segmented image on a target domain which is not seen, and the segmentation method can generalize various remote sensing images which are not seen so as to obtain better segmentation capability.

The invention discloses a causal reasoning-based satellite-ground collaborative remote sensing image interpretation method and device, relates to the technical field of remote sensing image processing, and mainly aims to solve the problem that the inference requirement of environmental causes in a remote sensing image cannot be met in the prior art. Comprising the following steps: a ground end obtains initial interpretation features obtained by performing initial interpretation on multi-modal remote sensing data by a satellite-borne end, and interprets the initial interpretation features based on a first interpretation model of which model training is completed to obtain core interpretation features; determining causal variables based on the core interpretation features, and constructing a causal variable graph based on the causal variables; determining a cause label and an interpretation result of the label area according to the causal variable graph, and obtaining a verification result corresponding to the cause label and the interpretation result; and based on the knowledge distillation and the verification result, determining learning core features of the first interpretation model for updating training, so that a second interpretation model in the satellite-borne end coordinates the learning core features fed back based on the ground end.

The invention discloses a remote sensing image multi-modal incremental panoramic perception method and system, and the method comprises the steps: constructing a remote sensing image multi-modal panoramic perception model based on incremental learning, and the model comprises a cross-modal general image encoder module, a cross-modal interaction knowledge distillation module, a cross-modal image encoder module, a cross-modal image encoder module and a cross-modal image encoder module; a task bias pseudo-mark generation module and a symmetric query interaction collaborative classification module; performing incremental training on the model; and performing multi-level and multi-mode continuous interpretation on the remote sensing image by using the trained model, and completing cross-mode multiple tasks of pixel-level classification, instance-level segmentation and image-level description of the remote sensing image. The method breaks through the limitation that an existing remote sensing image interpretation method only realizes predefined single-task interpretation, expands an interpretation product from a single form to a pixel-level, instance-level and image-level multi-mode increment interpretation mode, and can realize brain-like perception of the remote sensing image; the dependence of a deep learning model on data storage and labeling is reduced, the model training and maintenance cost is reduced, and the intelligent degree is improved.

The invention discloses a breast cancer recurrence risk prediction method and system based on multi-modal data missing interpolation and gene interpretability enhancement. The method comprises the following steps: firstly, dynamically generating and complementing features of a missing mode by matching a generative adversarial network with a mode missing mask matrix; then, a feature screening mechanism driven by gene information is introduced, through a multi-task learning network, image feature extraction is supervised by using a gene expression tag in a model training process, and image features highly associated with recurrence-related genes are screened out; and finally, fusing the complemented multi-modal time sequence characteristics by adopting Transform, and outputting a recurrence risk probability. According to the method, the robust prediction performance can be realized under the condition of data missing, and meanwhile, image interpretation with a molecular biology basis is provided for the feature screening process of the model, so that the reliability and clinical acceptability of the whole system are enhanced.

The invention discloses a rain and sewage pipeline defect detection method and system based on distributed optical fibers. The method comprises the following steps: 1, laying sensing optical fibers in a pipeline; 2, calibrating an excitation position; 3, collecting and demodulating optical fiber vibration data; 4, performing defect identification and defect positioning on data processed by an intelligent algorithm; vibration signals are sent out through the excitation device, signal collection is carried out in combination with the distributed optical fiber sensing module, efficient recognition and accurate positioning of pipeline defects are achieved in cooperation with an intelligent detection algorithm, and hidden diseases which cannot be perceived by naked eyes can be recognized. By means of an intelligent defect recognition algorithm, signals collected by the optical fiber sensing module are deeply analyzed, the positions and the disease degrees of the defects in the pipeline can be automatically judged, dependence on manual image interpretation is avoided, and automation and intelligentization of pipeline defect detection are achieved. The system has the advantages of being high in adaptability, wide in coverage range, efficient, accurate, high in intelligent processing capacity, low in cost, high in efficiency and the like.

The invention discloses a multi-mode element learning remote sensing reconnaissance target identification method guided by a detection mechanism, and belongs to the field of computer vision and remote sensing image processing. The core of the method is as follows: multi-modal feature extraction and fusion guided by a detection mechanism: aiming at the electromagnetic scattering characteristic of an SAR image, the geometric texture characteristic of a visible light image and the thermal radiation characteristic of an infrared image, respectively designing a special feature extraction network, and introducing a cross-modal fusion module based on an attention mechanism, feature enhancement expression of physical information complementation is realized; according to the meta learning training normal form, an optimization-based meta learning device is constructed, and training is performed on a large number of multi-modal remote sensing task sets, so that the model obtains the capability of quickly learning new target categories from a small number of samples. According to the method, a physical detection mechanism and data-driven meta learning are combined, and the problems that a traditional method is low in recognition precision and poor in generalization ability in a complex reconnaissance scene with scarce samples, variable target types and difficulty in SAR image interpretation are effectively solved.

The invention relates to a remote sensing visual language large model training method and device based on unified reinforcement learning, and belongs to the technical field of artificial intelligence and remote sensing image processing, and the method comprises the steps: carrying out the preprocessing of an input remote sensing image and a text instruction, and extracting visual features and text features; performing modal alignment, and inputting the modal alignment result into a pre-trained large language model for supervised instruction fine adjustment to obtain a basic model; constructing a multi-dimensional deterministic unified reward module based on a truth value; and performing enhanced fine tuning on the basic model, calculating a reward value output in the group by using a unified reward module, and updating model parameters based on relative advantages to obtain an optimized remote sensing visual language large model. According to the method, a deterministic reward module is adopted, a value network is abandoned through group relative strategy optimization, the calculation cost is reduced, task-level indexes are directly optimized through a multi-dimensional unified reward function, and the precision and output normalization of remote sensing image interpretation are improved.

The invention discloses a large-model-driven high-resolution remote sensing image interpretation method, and belongs to the field of image processing and analysis. The method comprises the following steps: constructing a large-scale high-resolution remote sensing image data set; cleaning and labeling the data, and dividing a training set and a verification set; constructing a multi-modal remote sensing information intelligent extraction large model, and realizing extraction and collaboration of multi-modal remote sensing features; constructing a multi-modal remote sensing information fusion module to realize fusion of visible light coding features and thermal infrared coding features; and processing the fused multi-modal features based on an encoder of the current large language model to realize the interpretation of the high-resolution remote sensing image. By implementing the method provided by the invention, the interpretation accuracy of the high-resolution remote sensing image can be effectively improved.

The invention belongs to the crossing field of remote sensing image processing and machine learning technologies, and particularly relates to a high-resolution remote sensing image target integration machine learning interpretation method in a mine environment. Through the customized feature extraction method for the mine environment, information reflecting mine ground feature features can be accurately recognized and extracted, the method is combined with the optimized integrated machine learning model, the method better adapts to the particularity of the mine high-resolution remote sensing image, and the recognition precision of various targets in the mine environment is improved. According to the method, automation and intelligence of remote sensing image interpretation can be realized, manual participation is greatly reduced, interpretation time is shortened, dynamic changes of a mine environment can be quickly responded, remote sensing image interpretation efficiency is improved, and timeliness of mine management is improved. Mine ground feature priori knowledge is fully utilized for extraction and model optimization, so that the model can better adapt to image data of different mine environments and different time phases, and the applicability and stability of the model are effectively improved.

The invention provides a remote sensing image interpretation method based on large model reasoning and tool enhancement, which comprises the following steps: generating an initial reasoning node of a reasoning tree by using a large language model as an active decision maker based on system cue words and input remote sensing image features; based on the current inference tree state, the active decision maker executes inference and generates a new inference node, so that the inference tree is expanded; based on node scores, adopting a pruning strategy to eliminate reasoning paths with low scores; repeating the steps until a reasoning termination condition is met, and selecting a path with the highest accumulated score from the reasoning tree as an optimal reasoning path; and key information is extracted and organized into a structured intelligence report to be output. According to the method, a complete interpretation chain of'thinking-tool-observation 'is explicitly modeled through a tree reasoning mechanism, so that the generation logic of each intelligence conclusion is clear and visible, and the problems of trust crisis and insufficient interpretability caused by'black box' decision of a traditional deep learning model are solved.

The invention discloses a landslide identification method, system, equipment and medium, and relates to the technical field of disaster detection, and the method comprises the steps: obtaining remote sensing image data and digital elevation data of a target slope section, and taking the data as a landslide data set; the method comprises the following steps: segmenting image data in a landslide data set into tokens with n * n pixels, inputting the token into a neural network model based on an encoder and decoder structure, and processing the token by an encoder and a decoder to obtain a token of a reconstructed image and reconstruction loss between the token and an original image token, and obtaining an optimal neural network model by taking the minimized reconstruction loss as a target, inputting remote sensing image data and digital elevation data of a real-time target slope section into the optimal neural network model for image interpretation, and obtaining a landslide map in the target slope section. According to the method, image interpretation is carried out by optimizing the neural network model, high precision is displayed through the image interpretation result of the region, and the application potential of the method in the field of disaster prevention and reduction is further verified.

The invention provides a linear motion constraint-based tail-section trajectory parameter surface-surface intersection measurement method, which comprises the following steps of: shooting and recording tail-section flight images of a flight target flying to a target from different angles by using at least two high-frame cameras in a staring measurement mode; retrieving the same-frame picture image of the flight target and the target according to the tail-section flight image, and playing back and outputting an encountering-section image in a real-time SDI video stream mode; reading multiple paths of playback SDI video streams, and performing real-time image interpretation by adopting a target detection algorithm to obtain interpretation data of a flight target; and carrying out filtering on the interpretation data by adopting a three-point random resampling method, carrying out angle synthesis to obtain target interpretation point data, and carrying out calculation on the target interpretation point data by adopting a surface-surface intersection algorithm based on linear motion constraint to obtain a tail-section flight path parameter of the flight target. According to the method, the tail section track parameters can be rapidly and accurately measured, equipment time synchronization is not needed, and the application range is wide.

The invention relates to the technical field of ecological monitoring, in particular to a resident-free island resource monitoring method based on a spatial three-dimensional model, and the method comprises the following steps: S1, building a remote sensing image interpretation information base; s2, based on the remote sensing image interpretation information base, generating an island space three-dimensional model containing island land terrain and shoreline distribution; s3, generating a total element three-dimensional dynamic map; s4, the interpretation result and the total element three-dimensional dynamic map in the S3 are superposed, and a reef area comprehensive distribution map is generated; s5, calculating the island resource development intensity and the ecological risk level, and outputting an island ecological assessment report; and S6, generating an island resource monitoring report. According to the invention, unmanned aerial vehicle aerial survey, GNSS measurement, depth sounding technology, image interpretation and ecological assessment are combined, a high-precision total-factor three-dimensional dynamic monitoring system is constructed, accurate analysis and visual expression of the current island resource utilization situation are realized, and scientific decision support is provided for island management and ecological protection.

The invention discloses a land coverage classification method based on inference segmentation, and relates to the technical field of remote sensing image inference segmentation and deep learning. Comprising the steps of training sample set establishment, multi-scale feature extraction submodule design, cross-modal feature fusion module design, land coverage classification model structure design based on reasoning segmentation, land coverage classification model training based on reasoning segmentation, model performance evaluation and index analysis. According to the method, effective combination of remote sensing image features and semantic information is realized, and the differentiated cognitive ability for different ground object targets is improved. Meanwhile, a remote sensing image land cover intelligent classification technology with practical value is obtained, the professional technical threshold is remarkably reduced, the intelligence and universality of remote sensing image interpretation are realized, and a convenient remote sensing information acquisition method is provided for users in various fields.

The invention provides a graph structure guided remote sensing image interpretation method and system, a terminal and a medium, and the method comprises the steps: carrying out the interpretation through employing a visual language model based on an obtained remote sensing image and expert priori knowledge; the model training process comprises the steps of obtaining a graph structure based on a vector graph corresponding to a remote sensing image, and rapidly synthesizing a large number of samples for training by randomly modifying the graph structure; model training is carried out based on the synthesized training remote sensing images and the corresponding training graph structures and training vector diagrams, graph structure features obtained by the training graph structures are adopted as training priori knowledge, a large number of image features obtained by the training remote sensing images are interpreted, and feedback optimization is carried out with the training vector diagrams as labels. The visual language model adopted by the invention can obtain the spatial information and semantic information of the remote sensing image, avoids inaccurate interpretation caused by spatial relationship deficiency, and has a better interpretation effect.

The invention relates to an image interpretation method and device based on a visual language model. The method comprises the following steps: determining a sample reasoning instruction according to a visual label of a sample image and a target knowledge graph; generating image interpretation information of the sample image based on the sample image and the sample reasoning instruction through a question and answer engine; training a to-be-trained visual language model based on the sample image, the sample reasoning instruction and image interpretation information of the sample image, and determining a target visual language model; and determining image interpretation information of the target image according to the target image and the target reasoning instruction through the target visual language model. According to the scheme, the data format of the model training data set is unified, the data set construction efficiency is improved, the labor cost is saved, and meanwhile, the trained visual language model can be subjected to deep knowledge reasoning.

The invention discloses an unmanned aerial vehicle autonomous path planning and high-precision surveying and mapping integrated system, and relates to the technical field of unmanned aerial vehicle surveying and mapping. The system comprises a terrain modeling module, a task configuration and scheduling module, a path planning module, an unmanned aerial vehicle flight control module, a surveying and mapping sensing module, a data caching and transmission module and a ground control and result processing module. The terrain modeling module constructs three-dimensional terrain modeling through remote sensing image interpretation and a digital elevation model; the path planning module fuses simulated annealing, an adaptive genetic algorithm and a large neighborhood search algorithm to realize route static optimization and multi-aircraft cooperative path generation; the flight control module autonomously completes flight according to the path instruction; the data caching module provides breakpoint resume and edge relay support; and the ground platform completes image processing, splicing modeling and result exporting. According to the invention, intelligent route planning and high-precision and multi-source surveying and mapping data acquisition under complex terrains are realized, and the surveying and mapping automation level and the data accuracy are remarkably improved.

The invention discloses a visual prompt multi-modal large model for multi-source remote sensing image interpretation, which belongs to the technical field of crossing of remote sensing and computer vision and comprises a multi-modal content coding and integration module, a cross-domain first-stage fusion training module, a pixel level visual positioning module and a large language model. The model supports the interpretation of a remote sensing image after the remote sensing image is arbitrarily amplified and reduced, and has flexible multi-granularity vision and language interaction capability. According to the model, a large language model is used as an interface, and multi-modal content integration including multi-sensor images, visual prompts and text instructions is achieved. In addition, two types of space tasks of anaphora understanding and visual positioning are unified into a visual prompt learning framework, and comprehensive and flexible multi-granularity understanding of remote sensing data is promoted.

The invention discloses a farmland protection forest area windproof effect evaluation method based on a land-gas coupling model, and relates to the technical field of agricultural science, high-precision topographic data of a target farmland protection forest area is collected and preprocessed, and spatial distribution information of a farmland protection forest is obtained by combining remote sensing image interpretation; on the basis of topographic data and spatial distribution information, topographic relief and earth surface coverage features are analyzed, and a refined underlying surface classification system is constructed; and configuring high-resolution grid parameters of a WRF mode, and coupling a Noah-MP land surface process model. According to the farmland protection forest area wind-proof effect evaluation method based on the land-gas coupling model, through combination of high-resolution topographic data and a multi-source remote sensing image, topographic relief and surface heterogeneity of a farmland protection forest area can be accurately depicted, and a WRF mode is coupled with a Noah-MP land surface process model; fine simulation of complex terrains and various underlying surfaces is realized, the limitation of traditional uniform underlying surface hypothesis is overcome, and the accuracy of windproof effect evaluation is remarkably improved.

The invention discloses an image interpretation method and device applied to natural resource investigation and monitoring. The method comprises the following steps: acquiring a to-be-interpreted remote sensing image and a current service scene; determining an interpretation model according to the current service scene; splitting the remote sensing image to be interpreted to obtain a plurality of remote sensing image slices; for each remote sensing image slice, inputting the remote sensing image slice into an interpretation model to obtain an interpretation result of the remote sensing image slice; and determining an interpretation result of the to-be-interpreted remote sensing image based on the interpretation result of the remote sensing image slice. The interpretation model can process remote sensing images in a unified mode, dependence on individual experience is eliminated, and it is ensured that interpretation results are stable and consistent. Moreover, the image is split into slices for parallel processing, so that the interpretation efficiency is greatly improved, and the time and cost of manual operation are reduced. The standardized algorithm of the model reduces the risk of misjudgment and missed judgment, can cope with complex scenes and detail requirements, and meets the increasingly refined monitoring requirements.

The invention discloses an image information interpretation method based on medical image multi-modal feature fusion, and relates to the technical field of medical image interpretation, and the method comprises the steps: obtaining the medical image data of at least two modalities of a target object, the medical image data of at least two modalities having a registration error; according to the method, feature extraction is performed on medical image data of at least two modes to obtain a feature set corresponding to each mode, and the feature set comprises structured features and local features which are robust to registration errors, so that the problem of misjudgment of the local features caused by the registration errors in traditional multi-mode fusion is solved, and the fusion accuracy is improved. And the accuracy of medical image interpretation is improved.

The invention relates to a surface mine surface monitoring method and device, and relates to the technical field of natural resource management, and the method comprises the steps: carrying out the data conversion and merging according to obtained multi-source monitoring data, obtaining pattern spot data and an interpretation remote sensing image, taking the pattern spot data as a reference, and carrying out the interpretation remote sensing image; performing statistical analysis of image interpretation according to the interpreted remote sensing image to obtain open-air surface coverage current situation data, and performing multi-dimensional statistical analysis and damage repair monitoring on the basis of the open-air surface coverage current situation data in combination with surface coverage classification data extracted from the multi-source monitoring data and abandoned mine data to obtain a corresponding statistical analysis result; and finally, performing multi-classification statistical analysis according to the statistical analysis result by adopting a chart combination mode to obtain a statistical analysis result representing the mine monitoring condition, thereby quickly and accurately obtaining the mining range, the damage condition and the restoration progress of the mine, and realizing accurate, comprehensive and accurate analysis of the mining and ecological restoration conditions. And efficient and dynamic monitoring is realized.

The invention provides a provincial domain level remote sensing AI big data natural resource intelligent monitoring system. The system comprises a data acquisition module, a data processing module, an intelligent interpretation module, a result verification module, a management application module and a communication interface module. The intelligent interpretation module integrates Segform semantic segmentation and a BIT change detection model, large-scale parallel interpretation is achieved by combining Redis cache management, celery task scheduling and MySQL process control under a GPU cluster architecture, batch image interpretation in a provincial domain range can be completed in a day-level period, linkage of interior work recheck and field work check is established through the achievement verification module, and large-scale parallel interpretation is achieved. And the management application module realizes the dynamic updating, thematic storage and cross-department sharing of the result, can quickly generate a change pattern spot distribution map, a cultivated land non-agrochemical and non-grain statistical table and a law enforcement check result, greatly shortens the interpretation period, and meets the requirement of dynamic supervision.

This invention provides an intelligent detection method and system for PI defects in chip packaging based on multimodal images, mainly relating to the field of chip packaging defect detection technology. The method comprises the following steps: First, at least two modal images of the chip packaging site are acquired, including a color image mainly characterizing the surface morphology of the chip and a fluorescence image mainly characterizing the physical properties of the chip surface material; then, the color image and fluorescence image to be detected are input into a multimodal target detection model to perform defect detection and obtain preliminary detection results; finally, based on the preliminary detection results and combined with the grayscale feature analysis of the fluorescence image, intelligent posterior decision-making is performed to obtain the intelligent detection result of PI defects in chip packaging. This invention combines the joint detection of color and fluorescence images with intelligent posterior decision-making, solving the problems of inaccurate detection of abnormal PI thickness defects, high dependence on manual image interpretation, and difficulty in automatically distinguishing multiple types of PI defects in existing technologies.

The present application relates to a kind of image processing methods for security inspection equipment, belong to security inspection technical field, it is difficult to meet the needs of image interpretation of image interpretation for the development of security inspection service and the image provided is difficult to meet the needs of third party integrated system problem.Method comprising the following steps: when the X-ray image of security inspection equipment is formed, the column data of the X-ray image formed is numbered in turn, and the row package image to which each column data belongs is judged;For each row package image, match the corresponding terminal of judging image, and send the column data of each row package image to the corresponding terminal of judging image, and the terminal of judging image is displayed according to the column data number received row package image, and centralized interpretation is carried out;The corresponding ray original image of each row package image is obtained, and each ray original image is uploaded to centralized storage;When third party integrated system needs to carry out image processing on row package image, corresponding ray original image is downloaded in centralized storage to carry out corresponding image processing.

The invention discloses a lung disease diagnosis and grading method based on quantitative CT image omics and deep learning. According to the method, through multi-dimensional information integration and model optimization, two core tasks of disease judgment and disease condition grading can be completed at the same time, and comprehensive support is provided for clinical diagnosis and treatment. The model sets feature priorities by referring to clinical diagnosis logic in the training process, the judgment result and the grading standard are completely matched with clinical general specifications, and the model can be directly applied to diagnosis and treatment decision-making without secondary conversion of doctors. After multi-center clinical verification and iterative optimization, the stability and the accuracy of the model are fully guaranteed, subjective errors caused by manual film reading can be effectively reduced, the diagnosis efficiency can be improved, the chronic obstructive pulmonary disease screening capability of primary medical institutions can be remarkably improved, early diagnosis and early treatment of more patients can be helped, and the clinical application prospect is wide. Therefore, the morbidity and disability rate of diseases are reduced, a clinical management path is optimized, and the overall disease burden is relieved.

The present invention provides a method for constructing a high-resolution remote sensing interpretation sample dataset, comprising the following steps: S101, acquiring high-resolution remote sensing image data using satellite remote sensing technology; S102, performing preprocessing operations on the high-resolution image data; S103, performing feature extraction on the preprocessed high-resolution image data; S104, performing image segmentation processing based on the feature-extracted high-resolution image data; S105, performing image interpretation based on the segmented high-resolution image data and constructing a sample dataset; S106, performing sample quality inspection, sample quantity inspection, and sample accuracy inspection on the sample dataset; and S107, establishing a multi-user sample database based on PostgreSQL to store and manage the sample dataset. The present invention realizes the construction of a high-resolution remote sensing image sample dataset to improve the accuracy of automatic interpretation of high-resolution image data.

The invention discloses a modeling method for the incidence relation between a deformation field and the erosion amount of a black soil erosion gully. According to the method, firstly, monitoring points are selected in black soil erosion gully areas with different landforms, high-precision equipment is used for collecting landform and hydrological data, and soil sample properties are analyzed; analyzing the deformation field, generating a digital elevation model, and determining the characteristics of the deformation field in combination with image interpretation; the erosion amount is calculated by adopting an improved erosion model, the deformation field and erosion amount data are integrated, and an incidence relation model is established by applying multiple methods. The model is verified through multi-source data fusion, and after improvement and perfection according to the result, the method is applied to black soil erosion gully dynamic monitoring, risk assessment and treatment planning. The method is comprehensive in data acquisition and deep in analysis, the model is accurate and effective, a scientific basis can be provided for black soil resource protection and sustainable utilization, and the problem of black soil erosion can be solved.