9 results about "Urban expansion" patented technology

The invention discloses a remote sensing image unsupervised change detection method and system based on structure perception and noise enhancement. According to the method, shared feature representation is extracted from a dual-temporal remote sensing image, a structure perception contrast learning mechanism is introduced to enhance the perception capability of a model for real geographic structure change, noise disturbance consistency constraint is designed to avoid an optimization shortcut problem, and a frequency attention decoding mechanism is adopted to finely depict a change region boundary. The system comprises a preprocessing module, a feature coding module, a structure sensing module, a noise disturbance module, a frequency attention decoding module and an unsupervised optimization module. According to the method, under the condition that manual labeling is not needed, the problems that an existing unsupervised change detection method is short in optimization, insufficient in semantic representation capacity, not fine in boundary description and the like are effectively solved, the accuracy and robustness of change detection are improved, and the method is suitable for the fields of urban expansion monitoring, disaster assessment, environment change analysis and the like.

The invention discloses a city expansion space-time prediction method and device, computer equipment and a storage medium, and relates to the field of remote sensing time sequence data analysis and geographic space artificial intelligence. The method comprises the following steps: acquiring city expansion data; performing serialization processing and spatio-temporal information embedding on the city expansion data to obtain time series data; inputting the time sequence data into a ViT encoder, and extracting global semantic features; inputting the global semantic features into a CNN decoder, and obtaining pixel-level city expansion prediction corresponding to a specified year; wherein a learnable time embedding vector is introduced into the CNN decoder, and is used for guiding the prediction of a specified year. According to the method, the strong global receptive field of the ViT encoder is utilized to capture long-distance spatial dependence, meanwhile, the CNN decoder is utilized to efficiently reconstruct high-resolution spatial details, in addition, a space-time embedding mechanism is introduced to effectively process complex spatial interaction and time dynamics, and high-precision, long-term and stable prediction is achieved.

This invention provides a method, system, computer equipment, and storage medium for correcting nighttime light remote sensing data, belonging to the field of urban environmental monitoring technology. The method includes: using a multi-source remote sensing sensor to statistically analyze the impermeable and non-impermeable layers in a target area, and calculating a multi-source data consistency index (CI); using the CI to correct the impermeable layer classification results of the target area; and based on the corrected impermeable layer classification results, counting the number N of impermeable layer pixels within the target area. b Number of pixels N in impermeable layer n , using N b and N n Calculate the proportion P of the impermeable layer within a unit pixel. b Using the nighttime light remote sensing brightness value NTL and the proportion of impermeable layer per unit pixel P b A calibration model was constructed to effectively reduce light spillover interference in impermeable areas, significantly improve the accuracy of impermeable layer data, and provide high-precision data support for urban expansion monitoring, light pollution assessment, and socio-economic activity analysis.

The application relates to the technical field of city planning, and discloses a micro-size city expansion measurement method based on a central grid area index, which comprises the following steps: constructing a basic grid, performing step-by-step spatial expansion on the basic grid to form multiple grids of different scales; dividing the research area into uniform grids by using the grids of different scales, then calculating the central grid area index CGAI corresponding to each grid under each scale, and formulating a screening rule based on the central grid area index CGAI, so as to screen the grids, and the remaining grids constitute the CGAI distribution map of the research area corresponding to the grid. The CGAI distribution maps of different scales are reasonably evaluated, and the CGAI distribution map with the highest evaluation result is taken as the final result output.

The invention discloses a remote sensing change detection method for urban expansion monitoring. The method comprises the following steps: step 1, collecting and sorting image samples containing urban expansion change; 2, performing image preprocessing on the images in the data set to enhance data diversity and improve the robustness of the model in a complex scene; 3, performing target labeling on the preprocessed image by adopting a LabelImg labeling tool, and dividing a data set into a training set, a verification set and a test set to ensure that the model can be trained and evaluated on different data sets; step 4, constructing a multi-dimensional feature alignment self-supervising pre-training network MDFANet used for urban expansion monitoring; and 5, inputting the preprocessed and labeled city expansion change data set into the MDFANet model for training, and learning the space and frequency domain features of the city building through forward propagation and reverse optimization, thereby improving the detection precision and generalization ability.

This invention provides a quantitative method for assessing the impact of urbanization expansion patterns on flood factors in small and medium-sized watersheds. This method is based on a coupling of three modules: quantification and clustering identification of urban expansion patterns, cross-validation mechanism of hydrological models and empirical formulas, and a pattern-process response analysis framework. This invention innovatively constructs a comprehensive quantitative index system for urban land use expansion patterns from three dimensions: level, rate, and pattern. By fully utilizing limited rainfall data and watershed geographic information, and through cross-validation using two methods based on different principles, it significantly improves the reliability and accuracy of flood parameter estimation in data-free areas.

The application relates to the technical field of remote sensing image processing, and particularly discloses a dynamic multi-scale change detection method based on a ground object unit and a change tendency. The application is characterized in that a dynamic self-adaptive scale mechanism of the ground object unit and the change tendency is constructed, the change tendency is judged through high-confidence-area screening and area-weighted statistics, multi-index collaborative judgment and a multi-feature clustering three-level fusion mechanism, the internal scale of the ground object unit is modulated based on a scale modulation factor of the ground object unit based on the change tendency, parameters such as weights are dynamically adjusted based on the change tendency, and fine classification is achieved by combining a multi-statistical threshold and hierarchical decision-making, so that the adaptability, classification accuracy and spatial integrity of the change detection in multiple scenes are significantly improved, and the application can be widely used in fields such as land use dynamic monitoring, ecological environment evaluation and urban expansion analysis.