83 results about "Land change" patented technology

The present invention belongs to the technical field of land change detection. Disclosed are a land change detection method combining matrix decomposition and adaptive propagation, and a system. A deep learning semantic segmentation model combining matrix decomposition and adaptive propagation is used for detecting a land change. The model is of a twin double-branch structure, which can receive both an input of a pre-temporal remote sensing image data set and an input of a post-temporal remote sensing image data set and can share network parameters, and which has the advantage of end-to-end feature learning. In the present invention, an attention method based on matrix decomposition is used to replace a conventional attention method based on manual design; moreover, in view of adaptive propagation technology for pixel offsets of neighboring points, a deep learning twin differential semantic segmentation network is established to realize automatic and precise land utilization / land coverage change detection on the basis of high-resolution optical data.

The invention relates to the technical field of natural resource monitoring and spatial information processing, in particular to a method and a system for feeding back land utilization change based on a land spatio-temporal model. The method comprises the following steps: deploying multi-source land monitoring equipment, carrying out collaborative data acquisition and standardization processing, and constructing a land space-time reference data set; performing triple mapping on the land space-time reference data set to obtain a land semantic association graph; constructing a land utilization knowledge graph based on the land semantic association graph; constructing a land change detection initial model by using the land utilization knowledge graph; meanwhile, in a high-frequency change scene, such as an urban and rural ecologic zone or an ecological sensitive area, a traditional model is slow in response to short-term land utilization disturbance, automatic adjustment cannot be carried out through deviation feedback between historical errors and model output, and the reliability of the model in actual application scenes such as resource regulation and control is limited.

The invention relates to the field of geographic information technology and natural resource management, and discloses an automatic analysis system for land utilization flow change in land change investigation. The system comprises an object-level spatiotemporal spectrum tree reconstruction and data initialization module, a topological potential energy field construction module based on evolution entropy, a multi-scale geometric difference extraction and morphological analysis module, a variable stiffness topological adsorption and decoupling module based on a stiffness ratio, and a flow damping filtering and event serialization module driven by semantic rules. The system constructs a topological potential energy field by using a historical state vector and semantic stiffness, performs one-way adsorption, adjustment or retention on a difference polygon according to relative stiffness, and cleans invalid traffic based on a damping matrix. According to the method, through physical field simulation and semantic logic verification, high-frequency disturbance misjudgment is effectively eliminated, pattern spot boundary correction is ensured to conform to the legal weight, and the authenticity and logic self-consistency of change survey data are improved.

The invention relates to the technical field of value accounting, and discloses an ecological system total production value accounting method based on GIS spatial analysis, and the method comprises the steps: carrying out the accounting of regional basic data and satellite image data, and constructing a bidirectional dynamic semantic mapping library corresponding to land change investigation land parcel classification-ecological system type; internet of Things monitoring data and statistical report data are obtained, and a standardized data set is obtained through space-time fusion of spatial interpolation-boundary correction and a sliding window smoothing method; constructing an urban area-typical district double-level accounting index system, and synchronously calculating a real object quantity and a value quantity; a model correction factor is called based on a GIS, indexes are quantified to grid units, a result is obtained through neighborhood analysis, and after parameters are calibrated, a multi-year ecological value trend map is generated in combination with the GIS; and outputting a customized result according to scenes such as ecological compensation, and outputting a final result after the customized result is qualified through three-level quality control verification. According to the method, the requirements of practical affairs such as ecological compensation and EOD projects on accounting precision and practicability can be met.

The invention provides a remote sensing-based land utilization carbon emission evaluation method, device and equipment and a medium, and the method comprises the steps: carrying out the radiometric calibration and spatial registration of obtained multi-source remote sensing data, and obtaining a standardized raster data set; generating a land utilization classification grid map based on the standardized grid data set; generating a land change vector layer through a continuous multi-stage land utilization classification grid map; generating a dynamic evaluation unit vector boundary according to the land utilization classification grid map and the land change vector layer; correcting a vegetation carbon absorption coefficient and an industrial emission coefficient in real time based on remote sensing inversion data, and constructing a spatial carbon transfer model; and inputting the land utilization classification grid map, the dynamic evaluation unit vector boundary and the spatial carbon transfer model into a layered mixed carbon emission evaluation model to obtain a carbon emission intensity distribution map. By adopting the scheme, the carbon emission under different land utilization modes can be accurately evaluated, and the scientificity and policy applicability of regional carbon balance evaluation are improved.

The invention discloses an intelligent fusion processing method and system for forest and grass wet resource pattern spots and annual land change survey data, and belongs to the field of space-time big data in the natural resource industry, and the method comprises the steps: completing the pattern layer fusion based on the annual land change survey data in combination with forest and grass wet pattern spot survey results; superposing county and rural boundaries to form an administrative fusion layer; performing subcompartment division based on the remote sensing image; merging broken classes according to a principle of adjacent positions and similar types; eliminating a pattern spot sharp angle; land classes are refined, and attribute information is perfected; the public welfare forest range and the protection level are optimized; automatically checking and summarizing to form a result layer; and finally performing statistics to generate various thematic reports. According to the method, technologies such as database storage, spatial index construction and a fusion algorithm are adopted, the data processing efficiency is improved by more than 50%, and the sharp angle processing time is shortened; and the labor cost and the time investment are obviously reduced.

The invention relates to the technical field of land utilization change evaluation, in particular to a land change degree evaluation method and system based on gradient division and two-dimensional analysis and a storage medium. Dividing the to-be-evaluated land into a plurality of target sub-regions with different target gradients by adopting topographic position indexes; wherein the terrain position index is obtained by calculating an elevation value and a slope value of the land; and analyzing the target sub-regions from two evaluation dimensions including a terrain gradient effect dimension and a land gradient utilization characteristic dimension, and determining a land change degree evaluation result of the to-be-evaluated land based on the obtained terrain gradient effect evaluation result and the land gradient utilization characteristic dimension evaluation result of the plurality of target sub-regions. The objective of the invention is to solve the problem of how to carry out land utilization change evaluation on areas with obvious vertical zone performance and altitude gradient performance.

The invention discloses an intelligent redevelopment research method for low-efficiency land use, which comprises the following steps of: acquiring multi-source data including land change survey data, topographic data, urban planning data, remote sensing data, point-of-interest data, mobile signaling data and social economic data in a research area, and inputting the multi-source data into a land use identification model; obtaining a spatial distribution diagram of low-efficiency land in the research area and corresponding low-efficiency factors; inputting the spatial distribution diagram and the low-efficiency factors into a redevelopment potential calculation model to obtain a redevelopment comprehensive potential value of the low-efficiency land; a plurality of redevelopment schemes are obtained based on the redevelopment comprehensive potential value, the rigid constraint of the low-efficiency land during redevelopment and the redevelopment mode library; and obtaining respective corresponding benefit index difference values after the low-efficiency land is redeveloped by using the plurality of redevelopment schemes, and obtaining an optimal redevelopment scheme from the plurality of redevelopment schemes by using a multi-attribute decision algorithm and the benefit index difference values.

The invention relates to an intelligent monitoring method and system for unused land based on a full-process closed-loop supervision mechanism, and relates to the technical field of land resource management.The method comprises the steps that a task database is established by means of obtained land related data, image spot extraction is conducted on all images in the database through a change detection model, and image spots are obtained; the method comprises the following steps: extracting pattern spots, performing a series of processing such as optimization, checking and superposition on the extracted pattern spots to obtain data fusion pattern spots, processing the data fusion pattern spots through grading and time identification to obtain graded pattern spots, finally inputting the graded pattern spots into an established system of a full-process closed-loop supervision mechanism, and performing interpretation in the system through each monitoring module. And result backflow updating and intelligent monitoring processing are performed based on the interpretation result, so that the processing precision and efficiency of the monitored pattern spots are effectively improved, the land change condition is comprehensively tracked, and the problem of insufficient timeliness and accuracy of land supervision in the prior art is solved by combining active interpretation supervision.

The invention discloses an element quantitative fusion-based urban coastal zone landscape evaluation and prediction method, which comprises the following steps of delimiting a vector boundary of a target urban coastal zone based on land change data, and dividing an area covered by the vector boundary into a plurality of grid units; associating the preprocessed spatial element data to the corresponding grid units, and constructing a structured spatial database; establishing a multi-element quantitative index system, calculating a combined weight, and outputting a comprehensive evaluation score of each grid unit through weighted summation; calculating global and local Moran's I indexes based on the comprehensive evaluation score, and identifying a spatial aggregation region and an abnormal region with statistical significance; obtaining dynamic monitoring data in a coastal zone range of a target urban area, and updating the structured spatial database; and based on the updated structured spatial database, comprehensively evaluating the score and the recognition results of the spatial accumulation area and the abnormal area, and generating an evaluation and prediction result.

The invention is suitable for the technical field of sponge city rainwater resource management, and provides a sponge city rainfall analysis method and system based on ultra-long duration data, and the method comprises the steps: obtaining the real-time predicted precipitation of a target city extension region in a preset time period, setting the initial rainwater management efficiency for the target city extension region, and obtaining the initial rainwater management efficiency of the target city extension region; meanwhile, acquiring an underground water level fluctuation record, historical land coverage change data and a soil permeability historical data table of the target city expansion area; the rainwater management efficiency is optimized by combining multi-dimensional data such as underground water level fluctuation, soil permeability historical data and historical land coverage change information; the method comprises the following steps: firstly, accurately adjusting a primary correction factor of rainwater management efficiency by analyzing an underground water level change rate and combining with soil permeability deviation; secondly, the average slope of the historical land change trend is used as a secondary correction factor, and the rainwater management efficiency is further optimized.

The invention provides a territorial greening space marking method and system, a terminal and a storage medium, and relates to the technical field of territorial surveying and mapping, and the method comprises the steps: constructing a resource current situation database according to a latest territorial change survey result and a forest grass humidity survey monitoring result; the resource status database is converted into a raster image, seed points are set, similar pixel areas are combined, and the raster image is segmented into land map spots; taking the site factors as growth constraint conditions of the land pattern spots to obtain initial land parcel data; performing vectorization processing on the initial land parcel data to obtain territorial greening land map spots; constructing a control boundary database, superposing the control boundary database to the territorial greening land pattern spots, and removing overlapped pattern spots in the superposed image to obtain initial greening pattern spots; performing corrosion and expansion processing on the initial greening pattern spots, and performing structural element size adaptive adjustment on the initial greening pattern spots after corrosion and expansion processing to obtain vector result data; the method has the advantage that the accuracy of land greening space marking is improved.

The invention discloses a multi-spectrum and SAR cooperative sensing sea reclamation increment remote sensing detection method, and belongs to the technical field of land change remote sensing monitoring. According to the scheme, multi-spectrum and SAR satellite data are fused, the data advantages of optical images and synthetic aperture radar images are effectively combined, and through the wave band operation and threshold segmentation technology, the real-time performance of the sea reclamation increment is improved. According to the method, more detailed and reliable sea reclamation pattern spot information can be obtained, the spatial distribution and dynamic change of suspected sea reclamation pattern spots can be accurately identified, and the limitation that traditional optical remote sensing is interfered by cloudy and rainy weather is effectively overcome. The method is used for a computer program to execute and complete multi-source data fusion, change detection and sea reclamation suspected pattern spot detection and analysis, and can be widely applied to the fields of coastal zone ecological environment protection, coastal city planning management, ocean resource sustainable utilization, relevant policy making and the like. And a core technical support is provided for full-process monitoring and scientific decision making of the reclamation activity.

The invention relates to the technical field of geographic information processing, in particular to a field proof file information encryption tamper-proof method of a land change investigation technology, which comprises the following steps: using authorization authentication, and initializing a camera; acquiring attitude information of the equipment when the investigator takes a picture, and calculating a direction angle of the picture by combining with geographic position information of the equipment; the positioning map is integrated into the shot picture, and watermarking is carried out on the shot picture; writing the required geographic metadata into an image file of a shot picture, and encoding and packaging according to a standard format; according to the method, the technical requirements of all links are packaged into the SDK, it is guaranteed that the proof file meets the requirements, it is guaranteed that the data of the proof file is encrypted and safely transmitted, operation is convenient and fast, quality is controllable, and the method is easy to implement and popularize. And the land change investigation business field work proof efficiency is improved by more than 40%.

The invention relates to a land change dynamic monitoring system and method based on multi-mode remote sensing and incremental learning, and belongs to the field of remote sensing monitoring, change detection and machine learning. The system comprises a comparative learning driven feature decoupling module, a progressive knowledge migration module, a change sample self-calibration module, a multi-granularity early warning trigger module and a difference sample backtracking and memory enhancement module, the feature decoupling module constructs an orthogonal semantic and change space and standardizes feature mapping, and the knowledge migration module realizes model updating through teacher and student networks. The self-calibration module evaluates the quality of unmarked samples and automatically incorporates the unmarked samples into a training set, the early warning trigger module realizes graded early warning and sensitive land class weighting, and the backtracking and memory enhancement module stores difficult samples and performs periodic equilibrium training. Under the condition of a small number of new samples, the change recognition accuracy of the system reaches 85%, the false alarm rate is reduced by 62%, the key change detection rate is kept above 95%, and the marking cost is greatly saved.

The invention discloses a deep learning-oriented high-resolution remote sensing sample rapid manufacturing method, which comprises the following steps of: extracting corresponding data as a basic sample according to a mapping relationship between a target ground feature and stock data based on the existing stock data, including geographic national condition monitoring data and territorial change survey data; based on the existing semantic segmentation model, batch production of supplementary samples is carried out; performing manual screening and semi-automatic repairing on an initial sample obtained by combining the basic sample and the supplementary sample to obtain vector sample data, and then establishing a sample library which is efficient, accurate and easy to retrieve and manage; after information is input into a sample library, flexible retrieval and on-demand assembly of sample data are realized through a multi-dimensional query sample index. According to the invention, multi-source stock data can be activated, sample sources can be expanded, the sample manufacturing cost can be reduced, the manufacturing time can be shortened, and the sample manufacturing precision can be ensured.

The application discloses an AI-based land investigation evidence photograph identification model construction method, an identification method and equipment, and the method comprises the following steps: acquiring a training data set; using the preprocessed historical evidence photographs to train the constructed initial identification model, obtaining the target identification model corresponding to each region and each period, and the target identification model is used for deploying on each terminal equipment. Through the quality detection and classification identification of the collected evidence photographs by the target identification model deployed on the terminal equipment, the problem that the business level of field personnel is uneven in the land investigation evidence work, which leads to inaccurate land class identification in the evidence photograph shooting process, and the real-time judgment of the land class identification and the evidence photograph shooting quality in the evidence shooting process by using the AI technology is solved, so that the shooting evidence photograph quality is improved, the overall land change investigation result is improved, and the subsequent land investigation evidence work is effectively guided.

The application discloses a real-time land change statistical analysis method based on a table strategy, comprising the following steps: obtaining original image patches from different themes in an image, classifying the union of the original image patches according to a preset rule to obtain minimum unit image patches, and associating the minimum unit image patches with a space unit table, a version table and a change patch table; each minimum unit image patch is associated with a unique child node, the original image patch is associated with a unique parent node, and the child node is associated with the parent node with the existence of image patch overlap source; the change patch is superimposed with the union, the space unit table, the version table and the change patch table are updated according to the intersection relationship between the change patch and the minimum unit image patch. The application has the beneficial effects that only the statistical analysis logic needs to be maintained, the original vector data does not need to be sorted, the maintenance cost is reduced, and when the analysis demand is proposed, the statistical report can be directly output from the existing vector data layer, and the timeliness is high.

The invention relates to the technical field of data fusion, in particular to a land surveying and mapping data analysis system based on multi-source data fusion, which comprises a grid unification module, a fluctuation extraction module, a data rejection module, a region compression module and a fusion analysis module. According to the method, a space unit consistency basis is constructed through unified serial number comparison, multi-source measurement value serial number coordination is completed in combination with spatial position residual difference, stable serial numbers meeting the vertical precision requirement are recognized in a difference fluctuation extraction mode, and data availability is quantitatively judged through numerical value deviation frequency in the serial numbers and an abnormal proportion. Eliminating a low-roughness region based on region standard deviation change to effectively compress redundant space paragraphs, realizing terrain elevation information fusion and land change analysis through weighted calculation of a space measurement value, and realizing fusion expression of a land surveying and mapping result; and the measurement precision consistency, the spatial feature expression capability and the change trend identification effect under multi-source topographic data fusion are effectively improved.

The application discloses a method for intelligent verification of land change evidence photos based on multi-source information fusion, and has the characteristics that a to-be-verified image set, a travel database and a land type database are acquired respectively; travel features are determined according to the to-be-verified image set and the travel database; whether the to-be-verified image set is abnormal is judged according to the travel features and the travel database; when the to-be-verified image set is abnormal, the to-be-verified image set is marked as wrong; when the to-be-verified image set is not abnormal, first land feature types in a to-be-verified area are determined according to the land type database; second land feature types in each photo in the to-be-verified image set are determined according to the photos; and qualified to-be-verified image sets are determined according to the first land feature types in the to-be-verified area and the second land feature types in each photo in the to-be-verified image set. The application constructs a multi-level automatic verification framework, reduces the problem of false evidence from the technical level, and effectively improves the verification efficiency of evidence photos.

This application relates to the technical field of land use change evaluation, and particularly to a land change degree evaluation method, system and storage medium based on gradient division and two-dimensional analysis. The land to be evaluated is divided into target sub-regions with multiple different target gradients by using the topographic position index; wherein, the topographic position index is calculated from the elevation value and slope value of the land; the target sub-regions are analyzed from two evaluation dimensions, namely the topographic gradient effect dimension and the land gradient utilization characteristic dimension, and based on the evaluation results of the topographic gradient effect and the evaluation results of the land gradient utilization characteristic dimension of the obtained multiple target sub-regions, the evaluation result of the land change degree of the land to be evaluated is determined. It aims to solve the problem of how to evaluate land use change in areas with obvious vertical zonality and altitude gradient.

The invention relates to a tilled land change detection and identification method and device based on a time sequence feature vector, belongs to the technical field of agricultural tilled land monitoring, and solves the problems that a tilled land change time node cannot be accurately determined and a part of tilled land change remote sensing weak signals are difficult to judge in an existing remote sensing technology. According to the tilled land change detection and identification method based on the time sequence feature vector, after remote sensing image data and multi-source land utilization data are obtained, multi-period harmonic fitting of the remote sensing image data is carried out based on a time domain sliding window, tilled land change vectors are constructed, and relative change vectors of the tilled land change vectors in adjacent periods are calculated; determining a potential cultivated land change area; dividing the potential cultivated land change area into a cultivated land area and a non-cultivated land area based on the training sample set, and generating a corresponding cultivated land classification result and a cultivated land probability; and combining cultivated land classification results to form a cultivated land change result of a continuous time sequence, and correcting the cultivated land change result in combination with the cultivated land probability. The detection accuracy of the cultivated land change dynamic state is improved.

The invention discloses a crop monitoring method based on remote sensing land utilization and land change data, which realizes macroscopic crop information capture and data preprocessing of a large-range farmland area through remote sensing satellite image data acquisition, effectively removes noise, corrects distortion and enhances features, provides a high-quality basis for classification and identification, and improves the quality of crops. The customized model keeps the feature extraction capability and adapts to the crop classification task by freezing the convolutional layer of the pre-training model and adding a new layer, and uses an optimizer and a loss function compilation model suitable for multi-class classification to ensure the training stability and convergence, so as to improve the crop classification efficiency. A large amount of preprocessed image data is trained to improve the generalization ability and classification precision of the model, the crop classification model can process remote sensing images in real time or quasi-real time, crop type information is output, intelligent and automatic monitoring, result analysis and output are achieved, and a visual distribution diagram and a detailed monitoring report are generated. And powerful data support is provided for agricultural production decision making.

The invention provides a territorial change investigation database quality inspection data processing method and system, and the method comprises the steps: obtaining physical feature information of a mobile storage medium storing territorial change investigation data, and carrying out the comparison verification of the physical feature information based on an authorized medium white list in a pre-stored security database, when the verification is passed, generating a medium authentication pass signal; in response to the medium authentication passing signal, performing automatic quality inspection on the land change survey original database in the mobile storage medium based on the auxiliary database, and generating a daily change survey data quality inspection report; correcting error data in the land change survey original database based on the daily change survey data quality inspection report to obtain a daily change survey result database; and performing data consistency comparative analysis on the obtained annual proof data and the daily change survey result database to obtain a territorial change survey data quality inspection result report. By adopting the method, the territorial change working efficiency can be improved.

The application provides a kind of weak supervision detection method of bulldozing area based on high-resolution remote sensing image, the method comprises: the original remote sensing image is preprocessed and feature extraction, obtain tile set and corresponding multi-scale feature map;Seed point mining is carried out to multi-scale feature map, and the seed point set of bulldozing area is obtained;Based on the seed point set of bulldozing area, the seed point corresponding to the multi-scale feature map is determined, and the complete region activation map is generated by expanding from the seed point;Based on the original remote sensing image and multi-scale feature map, the complete region activation map is processed by multi-feature boundary perception algorithm, and the pseudo label corresponding to the boundary position is obtained;Based on tile set and pseudo label, the new remote sensing image is processed by semantic segmentation network, and the detection result corresponding to the new remote sensing image of bulldozing area is obtained.The application realizes land use survey, illegal land use law enforcement inspection and land change dynamic monitoring by only relying on pixel level bulldozing area accurate detection of image level label.

The present application relates to a method, system, and medium for generating land use change images. The generation method includes: inputting first noise information and first feature information into a diffusion model, so that the diffusion model outputs predicted second noise information; the first feature information includes a change mask, a diffusion time step, a time label, and a ground feature category label; generating a target image based on the second noise information; the target image indicates an image before or after the land change. This method can utilize the diffusion model to construct an association representation between active and passive features through a collaborative mechanism using noise information and feature information, thereby generating before-after image sample pairs with strong realism and significant differences, effectively solving the overfitting and underfitting problems caused by sample scarcity in the training stage of deep learning models, and thus improving the quality and quantity of training samples.

This invention relates to the field of land resource survey technology, specifically to a method for automated verification and evidence generation of land change map features. Compared with existing technologies, this solution has the following advantages: 1. Significantly improved verification efficiency: Through automated map feature matching and task distribution, the response efficiency of evidence collection tasks is improved, which can meet the timeliness requirements of large-scale land change verification; 2. Enhanced support for on-site verification and evidence collection: Mobile terminals can upload evidence data during the evidence collection process, including evidence images, evidence videos, evidence locations, and verification opinions, reducing verification omissions and misjudgments; 3. Improved traceability: A closed-loop process of "task distribution - on-site evidence collection - data feedback - result archiving" is constructed. Based on the evidence data, a traceable land change survey result data package is finally formed. The data package is associated with the map feature area, avoiding data loss or matching errors. The entire chain is traceable, reducing the cost of cross-departmental collaborative communication.

The application relates to the technical field of value accounting, and discloses an ecosystem gross domestic product accounting method based on GIS spatial analysis, which comprises the following steps: accounting for regional basic data and satellite image data, constructing a two-way dynamic semantic mapping library corresponding to land change survey block classification-ecosystem type; obtaining Internet of Things monitoring data and statistical report data, performing spatial interpolation-boundary correction and sliding window smoothing space-time fusion to obtain a standardized data set; constructing a double-level accounting index system of urban areas and typical areas, and synchronously calculating physical quantity and value quantity; based on GIS, a model correction factor is called to quantize the index to a grid unit, neighborhood analysis is performed to obtain a result, and after the parameters are calibrated, a multi-year ecological value trend graph is generated in combination with GIS; customized results are output according to scenes such as ecological compensation, and after three-level quality control verification, the final results are output. The application can meet the needs of accounting accuracy and practicability of ecological compensation, EOD projects and the like.

The application provides a land greening space marking method, system, terminal and storage medium, relates to the technical field of land surveying and mapping, and comprises the following steps: constructing a resource status database according to the latest land change survey results and the forest and grass wet survey monitoring results; converting the resource status database into a grid image, setting a seed point, merging similar pixel regions, and segmenting the grid image into land map patches; taking site factors as growth constraints of the land map patches to obtain initial plot data; performing vectorization processing on the initial plot data to obtain land greening land map patches; constructing a control boundary database and superimposing the control boundary database on the land greening land map patches, removing overlapping map patches in the superimposed image, and obtaining initial greening map patches; performing erosion and expansion processing on the initial greening map patches, and adaptively adjusting the size of a structural element of the initial greening map patches after the erosion and expansion processing to obtain vector achievement data; and the application has the advantages of improving the accuracy of land greening space marking.

The invention discloses an AI-based territorial change investigation proof system and method, and belongs to the technical field of territorial investigation. The method specifically comprises the following steps: S1, multi-source data acquisition: synchronously acquiring a satellite remote sensing image, an unmanned aerial vehicle aerial image, GIS basic geographic data and mobile terminal field acquisition data of a land investigation area; s2, historical data preprocessing: taking land investigation historical reference data, and sequentially performing denoising, coordinate unification, format standardization, data enhancement and sample labeling processing to generate a training data set and a reference comparison data set; by unifying a coordinate system, a data format and de-noising enhancement processing, the problem of inconsistency of multi-source data is solved, manual operation errors are reduced, and data consistency of large-scale investigation is guaranteed. A hybrid model of CNN, Transform and U-Net is fused, an attention mechanism and a feature pyramid structure are matched, the change area recognition precision and the boundary segmentation effect are improved, and the manual intervention cost is greatly reduced through the automatic process.