Modeling spatial uncertainties in geospatial data fusion and mining

Geospatial data analysis relies on Spatial Data Fusion and Mining (SDFM), which heavily depend on topology and geometry of spatial objects. Capturing and representing geometric characteristics such as orientation, shape, proximity, similarity, and their measurement are of the highest interest in SDFM. Representation of uncertain and dynamically changing topological structure of spatial objects including social and communication networks, roads and waterways under the influence of noise, obstacles, temporary loss of communication, and other factors. is another challenge. Spatial distribution of the dynamic network is a complex and dynamic mixture of its topology and geometry. Historically, separation of topology and geometry in mathematics was motivated by the need to separate the invariant part of the spatial distribution (topology) from the less invariant part (geometry). The geometric characteristics such as orientation, shape, and proximity are not invariant. This separation between geometry and topology was done under the assumption that the topological structure is certain and does not change over time. New challenges to deal with the dynamic and uncertain topological structure require a reexamination of this fundamental assumption. In the previous work we proposed a dynamic logic methodology for capturing, representing, and recording uncertain and dynamic topology and geometry jointly for spatial data fusion and mining. This work presents a further elaboration and formalization of this methodology as well as its application for modeling vector-to-vector and raster-to-vector conflation/registration problems and automated feature extraction from the imagery

Guidance in feature extraction to resolve uncertainty

Automated Feature Extraction (AFE) plays a critical role in image understanding. Often the imagery analysts extract features better than AFE algorithms do, because analysts use additional information. The extraction and processing of this information can be more complex than the original AFE task, and that leads to the “complexity trap”. This can happen when the shadow from the buildings guides the extraction of buildings and roads. This work proposes an AFE algorithm to extract roads and trails by using the GMTI/GPS tracking information and older inaccurate maps of roads and trails as AFE guides

Automated Vector-to-Raster Image Registration

The variability of panchromatic and multispectral images, vector data (maps) and DEM models is growing. Accordingly, the requests and challenges are growing to correlate, match, co-register, and fuse them. Data to be integrated may have inaccurate and contradictory geo-references or not have them at all. Alignment of vector (feature) and raster (image) geospatial data is a difficult and time-consuming process when transformational relationships between the two are nonlinear. The robust solutions and commercial software products that address current challenges do not yet exist. In the proposed approach for Vector-to-Raster Registration (VRR) the candidate features are auto-extracted from imagery, vectorized, and compared against existing vector layer(s) to be registered. Given that available automated feature extraction (AFE) methods quite often produce false features and miss some features, we use additional information to improve AFE. This information is the existing vector data, but the vector data are not perfect as well. To deal with this problem the VRR process uses an algebraic structural algorithm (ASA), similarity transformation of local features algorithm (STLF), and a multi-loop process that repeats (AFE-VRR) process several times. The experiments show that it was successful in registering road vectors to commercial panchromatic and multi-spectral imagery

Toward virtual data scientist with visual means

The Big data challenge includes dealing with a big number of heterogeneous and multidimensional datasets of all possible sizes not only with data of big size. As a result a huge number of Machine Learning (ML) tasks, which must be solved dramatically exceeds the number of data scientists who can solve these tasks. Next many ML tasks require critical input from subject matter experts (SME) and end users/decision makers who are not ML experts. A set of tools that we call a “virtual data scientist” is needed to assist SMEs and end users to construct ML models for their tasks to meet this Big data challenge with a minimal contribution from data scientists. This paper describes our vision of such a “virtual data scientist” based on the visual approach with collocated and shifted paired coordinates. The approach is illustrated with real world data and ML tasks, as well as simulated data

Virtual Experts for Imagery Registration and Conflation

Abstract. The unique human expertise in imagery analysis should be preserved and shared with other imagery analysts to improve image analysis and decision-making. Such knowledge can serve as a corporate memory and be a base for an imagery virtual expert. The core problem in reaching this goal is constructing a methodology and tools that can assist in building the knowledge base of imagery analysis. This paper provides a framework for an imagery virtual expert system that supports imagery registration and conflation tasks. The approach involves two strategies: (1) recording expertise onthe-fly and (2) extracting information from the expert system in an optimized way. The second strategy is based on the theory of monotone Boolean functions