Automated Residential Area Generalization: Combination of Knowledge-Based Framework and Similarity Measurement

The major reason that the fully automated generalization of residential areas has not been achieved to date is that it is difficult to acquire the knowledge that is required for automated generalization and for the calculation of spatial similarity degrees between map objects at different scales. Furthermore, little attention has been given to generalization methods with a scale reduction that is larger than two-fold. To fill this gap, this article develops a hybrid approach that combines two existing methods to generalize residential areas that range from 1:10,000 to 1:50,000. The two existing methods are Boffet’s method for free space acquisition and kernel density analysis for city hotspot detection. Using both methods, the proposed approach follows a knowledge-based framework by implementing map analysis and spatial similarity measurements in a multiscale map space. First, the knowledge required for residential area generalization is obtained by analyzing multiscale residential areas and their corresponding contributions. Second, residential area generalization is divided into two subprocesses: free space acquisition and urban area outer boundary determination. Then, important parameters for the two subprocesses are obtained through map analysis and similarity measurements, reflecting the knowledge that is hidden in the cartographer’s mind. Using this acquired knowledge, complete generalization steps are formed. The proposed approach is tested using multiscale datasets from Lanzhou City. The experimental results demonstrate that our method is better than the traditional methods in terms of location precision and actuality. The approach is robust, comparatively insensitive to the noise of the small buildings beyond urban areas, and easy to implement in GIS software.

Minimizing uncertainty in complete automation of Douglas-Peucker Algorithm for geospatial mapping

It is qualitatively evident that the greater the map scale change, the greater the optimal distance threshold of the Douglas-Peucker Algorithm, which is used in polyline simplification. However, no specific quantitative relationships between them are known by far, causing uncertainties in complete automation of the algorithm. To fill this gap, the current paper constructs quantitative relationships based on the spatial similarity theories of polylines. A quantitative spatial similarity relationship model was proposed and evaluated by setting two groups of control experiments and taking <C, T> as coordinates. In order to realize the automatic generalization of the polyline, we verified whether these quantitative relationships could be fitted using the same function with the same coefficients. The experiments revealed that the unary quadratic function is the best, whether the polylines were derived from different or the same geographical feature area(s). The results also show that using the same optimal distance threshold is unreasonable to simplify all polylines from different geographical feature areas. On the other hand, the same geographical feature area polylines could be simplified using the same optimal distance threshold. The uncertainties were assessed by evaluating the automated generalization results for position and geometric accuracy perspectives using polylines from the same geographic feature areas. It is demonstrated that in addition to maintaining the geographical features, the proposed model maintains the shape characteristics of polylines. Limiting the uncertainties would support the realization of completely automatic generalization of polylines and the construction of vector map geodatabases.

Network Bending: Expressive Manipulation of Generative Models in Multiple Domains

This paper presents the network bending framework, a new approach for manipulating and interacting with deep generative models. We present a comprehensive set of deterministic transformations that can be inserted as distinct layers into the computational graph of a trained generative neural network and applied during inference. In addition, we present a novel algorithm for analysing the deep generative model and clustering features based on their spatial activation maps. This allows features to be grouped together based on spatial similarity in an unsupervised fashion. This results in the meaningful manipulation of sets of features that correspond to the generation of a broad array of semantically significant features of the generated results. We outline this framework, demonstrating our results on deep generative models for both image and audio domains. We show how it allows for the direct manipulation of semantically meaningful aspects of the generative process as well as allowing for a broad range of expressive outcomes.

Spatial Similarity of MRI-Visible Perivascular Spaces in Healthy Young Adult Twins

Purpose This study aims to determine whether genetic factors affect the location of dilated perivascular spaces (dPVS) by comparing healthy young twins and non-twin (NT) siblings. Methods A total of 700 healthy young adult twins and NT siblings (138 monozygotic (MZ) twin pairs, 79 dizygotic (DZ) twin pairs, and 133 NT sibling pairs) were collected from the Human Connectome Project dataset. dPVS was automatically segmented and normalized to standard space. Then, spatial similarity indices (mean squared error [MSE], structural similarity [SSIM], and dice similarity [DS]) were calculated for dPVS in the basal ganglia (BGdPVS) and white matter (WMdPVS) between paired subjects before and after propensity score matching of dPVS volumes between groups. Within-pair correlations for the regional volumes of dVPS were also assessed using the intraclass correlation coefficient (ICC). Results The spatial similarity of dPVS was significantly higher in MZ twins (higher DS [median, 0.382 and 0.310] and SSIM [0.963 and 0.887] and lower MSE [0.005 and 0.005] for BGdPVS and WMdPVS, respectively) than DZ twins (DS [0.121 and 0.119], SSIM [0.941 and 0.868], and MSE [0.010 and 0.011]) and NT siblings (DS [0.106 and 0.097], SSIM [0.924 and 0.848], and MSE [0.016 and 0.017]). No significant difference was found between DZ twins and NT siblings. Similar results were found even after subjects were matched according to dPVS volume. Regional dPVS volumes were also more correlated within pairs in MZ twins than DZ twins and NT siblings. Conclusion Our results suggest that genetic factors affect the location of dPVS.

Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow

Automated segmentation templates can save clinicians time compared to de novo segmentation but may still take substantial time to review and correct. It has not been thoroughly investigated which automated segmentation-corrected segmentation similarity metrics best predict clinician correction time. Bilateral thoracic cavity volumes in 329 CT scans were segmented by a UNet-inspired deep learning segmentation tool and subsequently corrected by a fourth-year medical student. Eight spatial similarity metrics were calculated between the automated and corrected segmentations and associated with correction times using Spearman’s rank correlation coefficients. Nine clinical variables were also associated with metrics and correction times using Spearman’s rank correlation coefficients or Mann–Whitney U tests. The added path length, false negative path length, and surface Dice similarity coefficient correlated better with correction time than traditional metrics, including the popular volumetric Dice similarity coefficient (respectively ρ = 0.69, ρ = 0.65, ρ = − 0.48 versus ρ = − 0.25; correlation p values < 0.001). Clinical variables poorly represented in the autosegmentation tool’s training data were often associated with decreased accuracy but not necessarily with prolonged correction time. Metrics used to develop and evaluate autosegmentation tools should correlate with clinical time saved. To our knowledge, this is only the second investigation of which metrics correlate with time saved. Validation of our findings is indicated in other anatomic sites and clinical workflows. Novel spatial similarity metrics may be preferable to traditional metrics for developing and evaluating autosegmentation tools that are intended to save clinicians time.

A generic test for the similarity of spatial data

Two spatial data sets are considered to be similar if they originate from the same stochastic process in terms of their spatial structure. Many tests have been developed over recent years to test the similarity of certain types of spatial data, such as spatial point patterns, geostatistical data and images. This research proposes a generic spatial similarity test able to handle various types of spatial data, for example images (modelled spatially), point patterns, marked point patterns, geostatistical data and lattice patterns. A simulation study is done in order to test the method for each spatial data set. After the simulation study, it was concluded that the proposed spatial similarity test is not sensitive to the user-defined resolution of the pixel image representation. From the simulation study, the proposed spatial similarity test performs well on lattice data, some of the unmarked point patterns and the marked point patterns with discrete marks. We illustrate this test on property prices in the City of Cape Town and the City of Johannesburg, South Africa.

GUIDED FEATURE MATCHING FOR MULTI-EPOCH HISTORICAL IMAGE BLOCKS POSE ESTIMATION

Historical aerial imagery plays an important role in providing unique information about evolution of our landscapes. It possesses many positive qualities such as high spatial resolution, stereoscopic configuration and short time interval. Self-calibration reamains a main bottleneck for achieving the intrinsic value of historical imagery, as it involves certain underdeveloped research points such as detecting inter-epoch tie-points. In this research, we present a novel algorithm to detecting inter-epoch tie-points in historical images which do not rely on any auxiliary data. Using SIFT-detected keypoints we perform matching across epochs by interchangeably estimating and imposing that points follow two mathematical models: at first a 2D spatial similarity, then a 3D spatial similarity. We import GCPs to quantitatively evaluate our results with Digital Elevation Models (DEM) of differences (abbreviated as DoD) in absolute reference frame, and compare the results of our method with other 2 methods that use either the traditional SIFT or few virtual GCPs. The experiments show that far more correct inter-epoch tie-points can be extracted with our guided technique. Qualitative and quantitative results are reported.