Search

Large wind farms are power plants that generate clean energy from a renewable source. They are increasingly being installed and operated to replace and complement fossil fuel power plants in an effort to help reduce greenhouse and other pollutant emissions (American Wind Energy Association, 2012 [1] ; American Wind Energy Association, 2011 [2] ; Global Wind Energy Council, 2011 [3] ; US Department of Energy, 2008 [4] ; Wiser et al., 2007 [5] ). Wind energy can have a positive economic impact and numerous locations on the planet are good candidates for wind energy production. Any direct environmental impact of large-scale wind farms needs to be investigated because it could impact agriculture, economics, health, society, and technology. A recent study showed that surface temperature is observed to increase directly downwind of large wind farms [6] . This research, performed concurrently, shows that similar and complementary results are obtained for a different location, and using remotely sensed temperature data obtained from a different satellite, at higher resolution and for a longer time span. Satellite remote sensing observations from Landsat 5 Thematic Mapper are used to study temperature changes over the San Gorgonio Pass Wind Farm from 1984 to 2011, with a pixel resolution of 120 m. A warming trend is consistently observed downwind of the wind farm.

Scene comparison and matching is a fundamental operation in geoinformatics. However, existing solutions are rather inadequate to support scene similarity assessment when comparing datasets collected from diverse sources especially ones that are available in diverse modalities (e.g. comparing image to vector datasets), or represent different time instances and thus differ partially in their content. In this paper we introduce a two-stage scene similarity assessment and matching framework that makes use of spatial scene content to compare and match two scenes as they may be captured in two different datasets (e.g. an aerial image and a map). At first stage our approach makes use of a matching algorithm based on the comparison of attributed graphs, where linear feature networks (e.g. road networks) are transformed into graphs and network properties are expressed through graph-embedded invariant attributes. By matching these graphs we can assess the similarity between two scenes. At the second stage, we proceed with an invariant scene comparison metric that incorporates additional scene content in the form of object configurations present within individual road network loops (e.g. building arrangements within city squares). By combining diverse but co-located pieces of information (e.g. roads and buildings) in an integrated process, our algorithm supports scene comparison and matching even when comparing heterogeneous datasets. In this paper we present key theoretical concepts and provide experimental results to demonstrate the performance of the proposed approach.

Summarization: In motion imagery-based tracking applications, it is common to extract locations of moving objects without any knowledge about the identity of the objects they correspond to. The identification of individual spatiotemporal trajectories from such data sets is far from trivial when these trajectories intersect in space, time, or attributes. In this paper, we present a novel approach for the reconstruction of entangled spatiotemporal trajectories of moving objects captured in motion imagery data sets. We have developed ACENT (Attribute-aided Classification of Entangled Trajectories), a novel framework that comprises classification, clustering, and neural net processes to progressively reconstruct elongated trajectories using as input spatiotemporal coordinates of image patches and corresponding attribute values. ACENT proceeds by first forming brief fragments and then linking them and adding points to them. An initial classification allows us to form brief segments corresponding to distinct objects. These segments are then linked together through clustering to form longer trajectories. Back-propagation neural network classification and geometric/self-organizing map (SOM) analysis refine these trajectories by removing misclassified and redistributing unassigned points. Thus, ACENT integrates some established classification and clustering tools to devise a novel approach that can address the tracking challenges of busy environments. Furthermore, ACENT allows us use spatiotemporal (ST) thresholds to cluster trajectories according to their spatial and temporal extent. In the paper, we present in detail our framework and experimental results that support the application potential of our approach. Presented on: ISPRS Journal of Photogrammetry and Remote Sensing

The extraction of road networks from digital imagery is a fundamental image analysis operation. Common problems encountered in automated road extraction include high sensitivity to typical scene clutter in high-resolution imagery, and inefficiency to meaningfully exploit multispectral imagery (MSI). With a ground sample distance (GSD) of less than 2 m per pixel, roads can be broadly described as elongated regions. We propose an approach of elongated region-based analysis for 2D road extraction from high-resolution imagery, which is suitable for MSI, and is insensitive to conventional edge definition. A self-organising road map (SORM) algorithm is presented, inspired from a specialised variation of Kohonen's self-organising map (SOM) neural network algorithm. A spectrally classified high-resolution image is assumed to be the input for our analysis. Our approach proceeds by performing spatial cluster analysis as a mid-level processing technique. This allows us to improve tolerance to road clutter in high-resolution images, and to minimise the effect on road extraction of common classification errors. This approach is designed in consideration of the emerging trend towards high-resolution multispectral sensors. Preliminary results demonstrate robust road extraction ability due to the non-local approach, when presented with noisy input.

In this paper, we address the problem of content-based image retrieval using queries on shape and topology. We focus on the particularities of image databases encountered in typical topographic applications and present the development of a spatial data management system that enables such queries. The query requires user-provided sketches of the shape and spatial configuration of the object (or objects) which should appear in the images to be retrieved. The objective of the search is to retrieve images that contain a configuration of objects sufficiently similar to the one specified in the query. Our approach combines the design of an integrated database with the development of a feature library and the necessary matching tools. In this paper, we present our overall scheme, introduce some individual database components, and provide some implementation results.

In this paper we present the development of a system prototype for sketch-based queries for the content-based retrieval of digital images from topographic databases. We discuss our overall strategy and associated algorithmic and implementation aspects, and we present associated database design issues. The query tools devised in this research are employing user-provided sketches of the shape and spatial configuration of the object(s) which should appear in the images to be retrieved. Our matching tool is inspired by least-squares matching (lsm) and represents an extension of lsm to function with a variety of raster representations. Our strategy makes use of a hierarchical organization of feature shapes within a feature library. The results are ranked according to statistical scores and the user can subsequently narrow or broaden his/her search according to the previously obtained results and the purpose of the search. Our approach combines the design of an integrated database environment with the development of a feature library and the necessary matching tools. We discuss our overall strategy and individual database components and present some implementation results.