1027 results about "Multi resolution" patented technology

A system and method are disclosed for generating hyperspectral images from multi-modal sensor data including RGB, LiDAR, thermal, and near-infrared inputs. Training data includes hyperspectral images and corresponding multi-modal measurements. Spectral band grouping is performed based on correlation coefficients. A multi-modal decomposition network with cross-modal attention mechanisms generate reconstructed hyperspectral images by fusing complementary sensor information. A fine-tuning network creates reconstructed RGB images. A comprehensive quality assurance system analyzes spectral consistency, cross-modal coherence, and fusion artifacts to generate quality metrics. Missing data compensation strategies handle corrupted sensor inputs using information from other modalities. The system includes temporal integration for video sequences and multi-resolution processing for different sensor resolutions. Quality metrics guide network weight adjustments to improve reconstruction accuracy while maintaining robustness to sensor failures and environmental variations.

The invention discloses an unmanned aerial vehicle routing inspection line adaptive obstacle detection method and system based on a monocular camera. The method comprises the steps of obtaining initial flight parameters, evaluating and optimizing the flight parameters based on scene complexity, collecting an original image sequence and carrying out quality evaluation and exposure compensation. Constructing a multi-resolution feature pyramid, extracting feature descriptors, and performing feature matching and verification; performing multi-constraint pose optimization and depth estimation based on a feature matching result, and reconstructing a three-dimensional point cloud; calculating direction enhancement response to extract a power line candidate area, performing multi-view line segment matching and three-dimensional reconstruction, and analyzing dynamic characteristics of the power line; and based on the power line three-dimensional model and the depth map, carrying out obstacle segmentation, calculating an obstacle distance, evaluating a risk level, and generating early warning information and a monitoring scheme. According to the invention, different scenes can be adaptively processed, the obstacle risk can be accurately identified and evaluated, and the inspection efficiency and reliability are improved.

The invention relates to the technical field of Internet of Vehicles, and discloses a holographic road network and Internet of Vehicles fusion method and device, equipment and a storage medium. The method comprises the steps of obtaining multi-source heterogeneous data collected by a target sensor, and performing adaptive multi-modal preprocessing on the multi-source heterogeneous data; performing space-time alignment and hierarchical structure recombination on the preprocessed multi-source heterogeneous data to obtain multi-source traffic recombination data; extracting multi-scale spatio-temporal features in the multi-source traffic recombination data, and performing multi-level semantic association, deep probability fusion and uncertainty modeling on the multi-source traffic recombination data based on the extracted traffic feature vector set and the preliminary dynamic traffic knowledge representation to obtain traffic perception probability distribution; and a high-precision dynamic semantic map is constructed based on traffic perception probability distribution, and fused multi-resolution map representation is obtained. According to the invention, the fusion of the holographic road network and the Internet of Vehicles multi-source data is realized, and the accuracy of the traffic characteristic data in the fusion result is improved.

Procedures, methods, architectures, apparatuses, systems, devices, and computer program products includes measuring Channel State Information (CSI) associated with at least one reference signal and determining a trained Artificial Intelligence (AI) model to generate at least a portion of a report that includes the CSI associated with the at least one reference signal. The report comprising the CSI associated with the at least one reference signal is transmitted.

The invention discloses a panchromatic sharpening method based on multi-resolution panchromatic feature guidance, which comprises the following steps: firstly, designing a multi-resolution feature extraction network based on a spatial frequency Transform module, and respectively extracting multi-scale features from panchromatic and up-sampled multispectral images; secondly, in order to restrain and optimize the extracted detail features, a multi-head self-attention-based texture injection module is adopted to extract a dependency relationship between panchromatic and multispectral features, and more similar detail features are guided to be injected; thirdly, in order to solve information loss caused by multispectral image up-sampling, multi-level detail features are injected into a multispectral reconstruction network, the detail features are learned step by step and reconstructed to a panchromatic image size, and a spectrum fusion module based on adaptive channel attention is utilized to reconstruct a fused image in a high-quality mode; and finally, optimizing the model performance by combining reconstruction loss based on L1 norm, structural similarity constraint and transmission perception loss in training. According to the method, the spectral fidelity and the spatial resolution of the fused image are remarkably improved.

The invention discloses an unmanned aerial vehicle accurate position landing method and system based on image recognition analysis, particularly relates to the technical field of aircraft landing, and aims to solve the problem of influence of jitter interference caused by micro airflow on landing precision. Identifying the jitter signal intensity and judging whether the jitter signal intensity is abnormal or not by detecting the posture change of the unmanned aerial vehicle in the landing process in real time; when the jitter is abnormal, carrying out multi-frame image superposition processing to generate a stable image sequence; detecting fine texture disturbance between adjacent frames based on multi-resolution fractal dimension analysis, and compensating non-linear influence caused by micro airflow; recording a jitter trajectory and calculating the relative displacement of the landing target through reverse trajectory reconstruction; extracting calibrated landing area features in combination with a convolutional neural network, generating an offset compensation value of a landing point, and dynamically correcting a landing path; according to the invention, the landing precision of the unmanned aerial vehicle in a complex environment is improved, and reliable guarantee is provided for automatic landing of the unmanned aerial vehicle.

The invention relates to the technical field of remote sensing image recognition, in particular to a remote sensing sewage area recognition method and system based on a graph structure and multi-stage enhancement. The method comprises the steps of performing data preprocessing and representation enhancement on an acquired remote sensing image; performing sewage salient region preliminary screening on the enhanced remote sensing image, including abnormal enhancement mapping construction based on local statistical distribution; pollution candidate graph extraction based on spatial structure prior driving; enhancing the response of the stable region based on a structure consistency enhancing mechanism of the polluted region; high-precision segmentation and identification of the sewage area comprises the following steps: constructing a multi-resolution residual pyramid structure; carrying out fine-grained boundary structure modeling and uncertainty suppression; generating a sewage distribution probability graph and optimizing structural consistency; according to the method, the multi-resolution residual pyramid structure is constructed, image context information under different perception scales is fully mined, and the sensitivity and edge integrity of the model to a sewage area under a complex texture background are remarkably enhanced.

The invention proposes a land utilization monitoring method and system based on remote sensing and big data, and relates to the technical field of land monitoring, and the method comprises the steps: dividing sub-regions, and obtaining the multi-temporal and multi-resolution remote sensing data of the sub-regions; performing feature extraction and classification on the remote sensing data based on a deep learning model to generate a land utilization classification map; based on the dual-temporal difference attention network, identifying a change area and constructing a change driving factor library fusing meteorological data and human activity data; detecting an abnormal area based on the driving factor library, and generating an abnormal type label and an attribution analysis report in combination with a dynamic early warning threshold; performing visual rendering on the monitoring result, and outputting an abnormal region early warning map and a disposal suggestion; high-precision feature extraction and classification are realized, change areas and driving factors are deeply analyzed, abnormal areas are effectively detected and early warning is performed, and the accuracy, timeliness and practicability of land utilization monitoring are improved.

A system and method for neural network weight compression with intrusion detection capabilities that optimizes model storage and transmission while providing security. The system analyzes weight characteristics to identify statistical properties within different neural network layers, generates optimized encoding schemes based on the analysis, and creates reference distributions for security verification. The compression process employs a multi-resolution approach that produces a progressive representation with base and enhancement layers, enabling flexible deployment across diverse computing environments. Security markers and statistical fingerprints can be embedded throughout the encoded representation, allowing for detection of unauthorized modifications during transmission or deployment. The system monitors encoded weight streams, measures distribution divergence against reference baselines, and generates alerts when statistical anomalies indicate potential tampering. This approach achieves superior compression ratios while maintaining model performance and providing robust protection against increasingly sophisticated attacks targeting neural network weights.

A high-quality digital human avatar is quickly reconstructed from a monocular video and is rendered in real time, the method is widely applied to various fields, and an existing method based on a neural radiation field is high in training and rendering cost. According to the dynamic human body three-dimensional modeling method based on the three-dimensional Gaussian sputtering technology, three-dimensional Gaussian can be expanded to a dynamic human scene, and rapid training and real-time rendering can be achieved. Specifically, the three-dimensional Gaussian of the human body is initialized in a standard space, and the three-dimensional Gaussian is converted from the standard space to a posture space by utilizing linear hybrid skin (LBS) according to posture parameters, so that fine details of the human body are learned at very low calculation cost; a posture parameter refining module and an LBS weight optimization module are designed to improve the problem of inaccurate detection of posture parameters in a real data set, and multi-resolution hash coding is introduced to accelerate the model training speed.

The invention discloses a high-fidelity large-scale scene rendering method, system and device based on Gaussian splashing and a medium. The method comprises the steps of S1, collecting high-precision point cloud data of a large-scale scene; s2, generating an initial three-dimensional grid model, and introducing depth prior information to reduce floating artifacts in a rendering process; s3, constructing a multi-resolution three-dimensional Gaussian expression model, and adopting a self-adaptive scaling factor and random resolution level training scheme in each level; s4, in the rendering process, a hierarchical selection strategy is adopted based on the distance between the three-dimensional Gaussian and a rendering viewpoint, and high-quality and high-speed rendering on high-performance equipment is achieved; and S5, developing a lightweight renderer, and completing efficient rendering on a lightweight mobile device. According to the invention, by introducing a depth regularization technology, high-precision point cloud data and three-dimensional Gaussian representation are combined; the three-dimensional Gaussian model is optimized by using depth prior information, floating artifacts possibly occurring in the rendering process are reduced, and the rendering quality is remarkably improved.

A high-fidelity three-dimensional reconstruction method for a specular reflection plane comprises the steps of decomposing pixel colors into diffuse reflection and specular reflection components based on a 3D Gaussian sphere, introducing a dynamic reflection ratio parameter and a spherical harmonic function coefficient to respectively represent two reflection characteristics, and simulating light multi-reflection behaviors through weight fusion of cumulative projection and a reflection ratio map. Secondly, in combination with monocular inverse depth calibration, depth smoothing constraint of color gradient weighting and edge mutual exclusion loss, geometric consistency is enhanced, and artifacts are suppressed; a progressive multi-resolution training strategy is further adopted, low resolution is gradually optimized to complete resolution, reflection parameters are activated in stages, 3D Gaussian sphere overgrowth and floating artifacts are inhibited, and efficiency and precision are balanced. According to the method, the reconstruction fidelity of the specular reflection scene is remarkably improved while the real-time rendering advantage of the 3DGS is reserved, and the method is suitable for the high-precision modeling fields of virtual reality, augmented reality and the like.

The invention discloses a medical form data identification method and system based on OCR and MLLM, and the method comprises the steps: receiving original image data, and generating an optimized image sequence through adaptive contrast enhancement, regional importance weighting and multi-resolution optimization; feature extraction is carried out based on the optimized image sequence, a text-space attention map is constructed, and text recognition is realized; performing visual coding and text coding on the optimized image sequence and the text recognition result, and obtaining uniform feature representation through feature alignment; constructing an information level diagram based on unified feature representation, performing relation reasoning, and combining professional knowledge verification to obtain structured feature information; and performing data verification on the structured feature information to obtain normalized data. According to the invention, efficient identification and accurate structuring of the medical form are realized, and the digitization efficiency of the medical data is improved.

The invention discloses a power system load prediction method based on a conditional guidance diffusion process, and belongs to the field of power system time sequence prediction. According to the method, a multi-stage progressive guide denoising process is designed, and the problem that the denoising process is uncontrollable is solved; mode features are effectively mined through a similar period load curve mining algorithm and multi-resolution trend features, and a mixed feature condition generation network is utilized to guide a multi-stage denoising process; and finally, designing a deep denoising network based on a Transform architecture, and improving the capability of restoring a load curve from Gaussian noise by the model through embedding a deep frequency domain decomposition module. According to the method, the denoising process is controllable, the fineness and accuracy of load curve generation are ensured, the interpretability of the model is enhanced, the precision of day-ahead load point prediction and deterministic prediction is remarkably improved, and more accurate prediction information can be provided for power system control and optimized operation.

The invention provides a mutual inductor test data cloud edge cooperative processing method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the time domain and frequency domain combined feature extraction of the original measurement data of a mutual inductor test through a multi-mode decoupling preprocessing model, and forming a data feature vector set, further generating a confidence label flow through state recognition and Bayesian inference, evaluating a prediction error, and realizing data classification screening and priority queue construction; the data are uploaded to a cloud end in a semantic compression and multi-resolution representation vector mode, so that the transmission efficiency is improved; carrying out deep modeling and model performance monitoring at the cloud, and if degradation is detected, returning edge original data to update the model; and finally, generating a scheduling weight factor according to the prediction error distribution graph, and dynamically optimizing an edge cloud task allocation proportion. According to the invention, the problem of low resource allocation efficiency caused by lack of a dynamic scheduling mechanism based on prediction errors and confidence driving in existing mutual inductor test data edge cloud cooperative processing can be solved.

The invention relates to the technical field of equipment anomaly detection, and discloses an anomaly detection method and system for an intelligent motor, and the method comprises the steps: collecting multi-source heterogeneous data when the intelligent motor works, carrying out the feature-level fusion, and carrying out the potential local learning, and obtaining optimized data features; performing data fragment segmentation on the optimized data features to obtain segmentation features, and determining a time-frequency feature spectrum of the segmentation features under multiple resolutions; mining a feature association relationship of the multi-dimensional operation features of the intelligent motor to construct a reinforcement learning strategy, and performing target feature screening on the multi-dimensional operation features of the intelligent motor to obtain a screened feature subset; and carrying out confrontation generation processing on the screening feature subset to obtain a simulation sample, carrying out feature ensemble learning processing on the simulation sample and the screening feature subset to obtain a judgment feature, and carrying out anomaly analysis on the intelligent motor to obtain an anomaly detection report. According to the invention, the detection precision of non-obvious or weak-feature anomalies in the intelligent motor can be improved.

The invention discloses a micro-grid dynamic scheduling method based on deep learning, and the method comprises the steps: fusing industrial Internet of Things collection and GIS positioning, and constructing a multivariable original spatio-temporal data set covering multiple nodes; extracting multi-scale features through multi-resolution wavelets and Fourier transform, combining the multi-scale features with a dynamic adjacency matrix, and realizing feature adaptive distribution and nonlinear dynamic modeling by using multi-scale attention gating, graph convolution and a time sequence neural network model; the micro-grid load and state prediction accuracy, the system generalization ability and the abnormal response level can be effectively improved, and powerful support is provided for intelligent scheduling and abnormal analysis.

The invention relates to the technical field of three-dimensional image reconstruction, in particular to a VR-based three-dimensional reconstruction method and system for textile culture heritage, and the method comprises the steps: obtaining a multi-view image collection sequence of the textile culture heritage, carrying out the geometric topological feature extraction of the multi-view image collection sequence, generating a three-dimensional point cloud geometric model, and carrying out the reconstruction of the three-dimensional point cloud geometric model. Performing multi-resolution texture feature mapping processing on the three-dimensional point cloud geometric model to generate an initial three-dimensional model fusing geometric topological features and multi-resolution texture features; performing dynamic geometric optimization processing on the initial three-dimensional model based on preset VR display parameters to generate a target three-dimensional model after surface continuity correction; and performing dynamic matching processing on the target three-dimensional model and a preset VR interaction algorithm to generate a VR visual model with a multi-dimensional interaction attribute, and transmitting the VR visual model to a VR display terminal to perform three-dimensional space rendering, so that the three-dimensional reconstruction and interaction intelligence degree of the textile culture heritage can be improved in combination with a VR technology.

The invention discloses a ship desulfurization system reliability evaluation method and system based on multi-source information fusion, and the method comprises the steps: collecting multi-source information data of a hybrid desulfurization system, and carrying out the self-adaptive preprocessing; generating a fusion feature vector; constructing a dynamic Bayesian network based on multi-source fusion features, performing real-time reasoning by adopting data-driven transition probability learning and particle filtering, describing transient behaviors of system state evolution and mode switching, and performing dynamic multi-state reliability modeling; a fault mode is automatically extracted, and data-driven systematic risks are identified and quantitatively analyzed; a multi-resolution digital twinborn architecture is constructed, dynamic simulation prediction is carried out, a self-adaptive updating mechanism is adopted to keep the model synchronous with a physical system, and a virtual verification environment for reliability evaluation is provided; according to the method, an intelligent decision optimization system is constructed, self-adaptive generation and dynamic adjustment of a maintenance strategy are realized, closed-loop feedback is carried out, and the accuracy of reliability evaluation of the hybrid desulfurization system is improved.

The invention discloses a panchromatic sharpening method based on multi-resolution panchromatic feature guidance, which comprises the following steps: firstly, designing a double-branch feature decomposition module for respectively performing feature decomposition on a panchromatic image and a multi-spectral image to extract low-frequency basic features and high-frequency detail features, and enabling the two branches to share the same shallow feature extraction module; correlation constraint is applied to the extracted basic features; then, aiming at the problem of information redundancy caused by direct serial connection and stacking of the extracted features, performing interactive fusion on the features by using an interactive feature recombination module, and enhancing a long-distance dependency relationship and a global context relationship among different modal features; and finally, an INN-based image reconstruction module is provided, and efficient fusion of the basic features and the detail features is realized through multi-level transformation and mapping. According to the method, cross-modal features can be effectively extracted and recombined and fused, spectrum distortion is remarkably reduced, and the spectrum distortion phenomenon caused by improper feature fusion is effectively avoided.

The invention provides a three-dimensional visual scene confrontation simulation deduction method and a related device. The method comprises the following steps: constructing a simulation deduction platform; creating simulation elements such as a multi-resolution construction resource model, a real equipment resource model mirror image, an equipment simulator resource model mirror image and the like and a target scene; configuring a target scene, selecting and configuring resources of two confrontation parties, arranging resource model task planning and collaborative logic, accessing state information of real equipment and an equipment simulator, and performing state logic calculation and situation deduction of scene confrontation; carrying out centralized management and time synchronization management on the system state information; dynamically generating a three-dimensional visual scene confrontation video and a plane situation video according to the real-time state of the system; deduction process comparison is carried out by adjusting resource configuration, task planning and collaborative logic of two confrontation parties, equipment type selection, configuration and layout schemes are verified and optimized, equipment task arrangement and collaborative schemes are verified and optimized, and decision support is provided for system application and engineering construction.

The invention provides an inventory management method and system for a library, and relates to the technical field of book management, and the method comprises the steps: obtaining the metadata of the library, including book information and user borrowing records, constructing a knowledge graph based on the data, and extracting a user query intention through a heterogeneous graph matching algorithm, and obtaining a target node set. Secondly, semantic representation information of the user intention is extracted based on multi-resolution wavelet transform and frequency domain analysis; and predicting future borrowing demands by using a sparse Bayesian learning algorithm to generate a demand prediction result. Through a multi-objective planning algorithm and a convex optimization method, in combination with inventory distribution and logistics cost, an inventory distribution scheme is generated, and the scheme is optimized through a Lagrange relaxation method. And finally, adjusting an inventory distribution scheme in real time by combining a dynamic system state estimation method. The accuracy and flexibility of library inventory management can be remarkably improved, the operation cost is reduced, and the resource utilization rate is improved.

The invention discloses a bridge structure surface crack multi-scale image segmentation and damage assessment method, and belongs to the technical field of bridge structure health monitoring, and the method comprises the steps: a multi-scale pyramid feature preprocessing step: carrying out the multi-resolution feature extraction of a bridge surface image; in the adaptive attention-guided crack segmentation step, crack region response is enhanced through a channel and space attention mechanism; the crack geometric parameter accurate quantification step is used for calculating the length, width, depth and direction of the crack; in the time sequence comparison crack development trend prediction step, the crack propagation rate is calculated according to the parameter difference value between the current detection data and the historical detection data divided by the time interval, and the development trend is predicted; in the multi-dimensional damage comprehensive evaluation step, damage scores are calculated, damage grades are determined, segmentation parameters are fed back and adjusted, and scientific data support is provided for bridge safety evaluation and maintenance decision making.

The invention relates to the technical field of image recognition, in particular to a stem cell classification method and system based on image segmentation. The method comprises the following steps: acquiring original stem cell image data, and performing Wasm processing on the original stem cell image data to obtain stem cell image quality parameters; performing adaptive preprocessing on the original stem cell image data based on the stem cell image quality parameters to obtain standardized stem cell image data; and performing multi-resolution pyramid construction on the standardized stem cell image data to obtain multi-level stem cell image representation data. According to the method, through multi-resolution pyramid construction, hierarchical processing of cell regions, accurate morphological feature extraction and hierarchical classification and distributed storage processing, the accuracy, reliability, adaptability and expandability of stem cell image classification are effectively improved.

The invention relates to the technical field of image analysis, in particular to an automatic marking robot calibration method based on visual guidance, which comprises the following steps of: establishing an image set comprising different resolution levels based on operation site image data acquired by a marking robot, and performing corner detection and straight line segment detection on each level image in parallel. According to the method, through a parallel detection mechanism of a multi-resolution hierarchical image set, angular point and straight line segment features under different scales are synchronously extracted, and a multi-scale feature point set with high robustness is constructed in combination with cross-hierarchical coordinate stability measurement and response intensity quantification. And performing dynamic screening and grouping association on the feature points based on a preset geometric constraint condition, eliminating noise interference and false detection features, and generating a candidate calibration structure set with spatial consistency. Weighted contribution value fitting is adopted, cross-scale stability and detection confidence of feature points are integrated, and geometric accuracy and anti-interference capability of calibration reference point coordinates are improved.

A computer-implemented method for generating a fixed-size N-dimensional vector representation for a given document is disclosed. The method comprises extracting text-portions from a plurality of documents, embedding the extracted text-portions into fixed-sized K-dimensional text-portion vectors, clustering the text-portion vectors into N clusters C_1, C_2, . . . , C_N, generating an N-dimensional document vector E(D) for a document D by (i) associating its nth coordinate value E(D)_n to the nth cluster C_n, and (ii) if not previously done for the document D extracting text-portions from the document D and embedding the extracted text-portions into K-dimensional text-portion vectors, and (iii) setting the values E(D)_n based on similarity matching score values between the text-portion vectors of the document D and text-portions vectors of the N clusters.

A CAE simulation result visualization system supporting multiple interaction modes comprises a multi-resolution rendering module used for obtaining interaction behaviors of a user and dynamically adjusting the resolution of a rendering area according to the interaction behaviors of the user; the key frame and time step screening module is used for analyzing the input simulation data, automatically screening out key frames and detecting the time step change of the simulation data according to a time sequence analysis method; the data compression and simplification module is used for compressing the data by using a feature-preserving compression algorithm, performing incremental storage and sparse representation on the compressed data, and recording data changes; and the dynamic local playing and frame-skipping rendering module is used for acquiring a user local time playing instruction, and processing user local time and non-user local time respectively through a frame-skipping rendering method so as to realize multi-frame rendering and switching. According to the method, the data analysis efficiency is improved, the rendering and data transmission efficiency is improved, and computing resources are saved.

The invention discloses a hydrogen energy system safety assessment method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing the dynamic operation parameters of a hydrogen energy system, and generating an input feature matrix; s2, inputting the feature matrix into a multi-resolution feature sensing module to generate a high-dimensional feature map; s3, inputting the high-dimensional feature map into a dynamic time bending network to generate a parameter incidence matrix; s4, optimizing feature weight distribution based on an adaptive differential evolution algorithm; s5, abnormal distribution is captured through the dynamic graph convolutional network, and a dynamic abnormal graph is generated; s6, simulating an anomaly propagation path, and generating a cascade anomaly analysis result in combination with a dynamic traceability mechanism; s7, a cascade anomaly analysis result is optimized through a grey wolf-firefly hybrid optimization algorithm; and S8, dynamic operation parameters are adjusted based on a final cascade risk prediction result, and hydrogen energy system safety assessment is completed. According to the method, deep learning, a hybrid optimization algorithm and the like are combined, and safety evaluation of abnormal detection and risk prediction of the hydrogen energy system is realized.

The invention relates to the technical field of remote sensing image segmentation, and discloses a remote sensing image segmentation method based on a foreground sensing network, and the method comprises the steps: constructing the foreground sensing network based on an encoder and decoder structure, and constructing a combined loss function; training the foreground sensing network; segmenting by using the trained foreground sensing network to obtain a category probability graph of each pixel; and based on the dynamic multi-resolution attention, performing multi-scale grading on the category probability graph in combination with the environmental characteristics, dynamically adapting a post-processing strategy, and performing post-processing according to the adapted post-processing strategy to optimize a segmentation result. According to the method, an end-to-end intelligent segmentation framework is constructed through multi-scale feature fusion, environment adaptive post-processing and geographical semantic constraint, the segmentation precision, robustness and practicability in a complex remote sensing scene are remarkably improved, and an efficient solution is provided for the fields of natural resource management, disaster emergency response and the like.

The invention discloses a building surveying and mapping system based on unmanned aerial vehicle remote sensing, and belongs to the technical field of building surveying and mapping. The system comprises a data acquisition module, a data preprocessing module, a feature extraction module, a spatial relationship analysis module and a dynamic change analysis module, and the data acquisition module is responsible for automatically acquiring multi-temporal remote sensing data covering a target area; the data preprocessing module is responsible for performing cleaning, geometric correction and multi-source data fusion on the acquired remote sensing data; the feature extraction module is responsible for extracting key features of the building from the multi-resolution remote sensing data; the spatial relationship analysis module analyzes the spatial distribution characteristics and the mutual relationship of the building group; and the dynamic change analysis module is responsible for monitoring and analyzing the dynamic change of the building group, including automatic identification of new building, dismantling and reconstruction conditions.