Ranga Raju Vatsavai

Monitoring Land-Cover Changes: A Machine-Learning Perspective

Monitoring land-cover changes is of prime importance for the effective planning and management of critical, natural and man-made resources. The growing availability of remote sensing data provides ample opportunities for monitoring land-cover changes on a global scale using machine-learning techniques. However, remote sensing data sets exhibit unique domain-specific properties that limit the usefulness of traditional machine-learning methods. This article presents a brief overview of these challenges from the perspective of machine learning and discusses some of the recent advances in machine learning that are relevant for addressing them. These approaches show promise for future research in the detection of land-cover change using machine-learning algorithms.

Semantics-Enabled Framework for Spatial Image Information Mining of Linked Earth Observation Data

Recent developments in sensor technology are contributing toward the tremendous growth of remote sensing (RS) archives (currently, at the petabyte scale). However, this data largely remain unexploited due to the current limitations in the data discovery, querying, and retrieval capabilities. This issue becomes exacerbated in disaster situations, where there is a need for rapid processing and retrieval of the affected areas. Furthermore, the retrieval of images based on the spatial configurations of affected regions [land use/cover (LULC) classes] in an image is important in disaster situations such as floods and earthquakes. The majority of existing Earth observation (EO) image information mining (IIM) systems does not consider the spatial relations among image regions during image retrieval (aka spatial semantic gap). In this work, we have specifically addressed two issues, i.e., explicit modeling of topological and directional relationships between image regions, and development of a resource description framework (RDF)-based spatial semantic graphs (SSGs). This enables more intuitive querying and reasoning on the archived data. A spatial IIM (SIIM) framework is proposed, which integrates a logic-based reasoning mechanism to extract the hidden spatial relationships (both topological and directional) and enables image retrieval based on spatial relationships. The system is tested using several spatial relations-based queries on the RS image repository of flood-affected areas to check its applicability in post flood scenario. Precision, recall, and F-measure metrics were used to evaluate the performance of the SIIM system, which showed good potential for spatial relations-based image retrieval.

Parallel Processing over Spatial-Temporal Datasets from Geo, Bio, Climate and Social Science Communities: A Research Roadmap

This vision paper reviews the current state-ofart and lays out emerging research challenges in parallel processing of spatial-temporal large datasets relevant to a variety of scientific communities. The spatio-temporal data, whether captured through remote sensors (global earth observations), ground and ocean sensors (e.g., soil moisture sensors, buoys), social media and hand-held, traffic-related sensors and cameras, medical imaging (e.g., MRI), or large scale simulations (e.g., climate) have always been “big.” A common thread among all these big collections of datasets is that they are spatial and temporal. Processing and analyzing these datasets requires high-performance computing (HPC) infrastructures. Various agencies, scientific communities and increasingly the society at large rely on spatial data management, analysis, and spatial data mining to gain insights and produce actionable plans. Therefore, an ecosystem of integrated and reliable software infrastructure is required for spatialtemporal big data management and analysis that will serve as crucial tools for solving a wide set of research problems from different scientific and engineering areas and to empower users with next-generation tools. This vision requires a multidisciplinary effort to significantly advance domain research and have a broad impact on the society. The areas of research discussed in this paper include (i) spatial data mining, (ii) data analytics over remote sensing data, (iii) processing medical images, (iv) spatial econometrics analyses, (v) Map-Reducebased systems for spatial computation and visualization, (vi) CyberGIS systems, and (vii) foundational parallel algorithms and data structures for polygonal datasets, and why HPC infrastructures, including harnessing graphics accelerators, are needed for time-critical applications.

Scalable nearest neighbor based hierarchical change detection framework for crop monitoring

Monitoring biomass over large geographic regions for changes in vegetation and cropping patterns is important for many applications. Changes in vegetation happen due to reasons ranging from climate change and damages to new government policies and regulations. Remote sensing imagery (multi-spectral and multi-temporal) is widely used in change pattern mapping studies. Existing bi-temporal change detection techniques are better suited for multi-spectral images and time series based techniques are more suited for analyzing multi-temporal images. A key contribution of this work is to define change as hierarchical rather than boolean. Based on this definition of change pattern, we developed a novel time series similarity based change detection framework for identifying inter-annual changes by exploiting phenological properties of growing crops from satellite time series imagery. The proposed framework consists of three components: hierarchical clustering tree construction, nearest neighbor based classification, and change detection using similarity hierarchy. Though the proposed approach is unsupervised, we present evaluation using manually induced change regions embedded in the real dataset. We compare our method with the widely used K-Means clustering and evaluation shows that K-Means over-detects changes in comparison to our proposed method.

Scalable Machine Learning Approaches for Neighborhood Classification Using Very High Resolution Remote Sensing Imagery

Urban neighborhood classification using very high resolution (VHR) remote sensing imagery is a challenging and {\em emerging} application. A semi-supervised learning approach for identifying neighborhoods is presented which employs superpixel tessellation representations of VHR imagery. The image representation utilizes homogeneous and irregularly shaped regions termed superpixels and derives novel features based on intensity histograms, geometry, corner and superpixel density and scale of tessellation. The semi-supervised learning approach uses a support vector machine (SVM) to obtain a preliminary classification which is then subsequently refined using graph Laplacian propagation. Several intermediate stages in the pipeline are presented to showcase the important features of this approach. We evaluated this approach on four different geographic settings with varying neighborhood types and compared it with the recent Gaussian Multiple Learning algorithm. This evaluation shows several advantages, including model building, accuracy, and efficiency which makes it a great choice for deployment in large scale applications like global human settlement mapping and population distribution (e.g., LandScan), and change detection.

Emerging trends in monitoring landscapes and energy infrastructures with big spatial data

Explosion of spatial data from satellite to citizen sensors has posed the critical challenge of Big Spatial Data integration, analysis, and visualization. This article focuses on research and development activities at Oak Ridge National Laboratory (ORNL) that are addressing end-user applications utilizing high performance computing based geospatial science and technology solutions to optimize the analysis, modeling, and multi-megapixel scale visualization of the geospatial data. Specifically we highlight recent developments and successes in the areas of high resolution settlement mapping, transportation and mobility analysis, and effective monitoring of biomass for energy and food security.

Data Science for Food, Energy and Water: A Workshop Report

At the 22nd ACM SIGKDD conference on Knowledge and Data Discovery (KDD), a workshop on Data Science for Food, Energy andWater (DSFEW) was held to foster an interdisciplinary community intersecting data science and societally important domains of food, energy and water. The workshop included keynotes, panel discussion, presentations and posters, and introduced the emerging area of DSFEW to ACM SIGKDD audience, and triggered interdisciplinary idea-sharing in DSFEW research. The workshop website is sites.google.com/site/2016dsfew.

Sliding Window-based Probabilistic Change Detection for Remote-sensed Images

A recent probabilistic change detection algorithm provides a way for assessing changes on remote-sensed images which is more robust to geometric and atmospheric errors than existing pixel-based methods. However, its grid (patch)-based change detection results in coarse-resolution change maps and often discretizes continuous changes that occur across grid boundaries. In this study, we propose a sliding window-based extension of the probabilistic change detection approach to overcome such artificial limitations.

pFUTURES: A Parallel Framework for Cellular Automaton Based Urban Growth Models

Simulating structural changes in landscape is a routine task in computational geography. Owing to advances in sensing and data collection technologies, geospatial data is becoming available at finer spatial and temporal resolutions. However, in practice, these large datasets impede land simulation based studies over large geographic regions due to computational and I/O challenges. The memory overhead of sequential implementations and long execution times further limit the possibilities of simulating future urban scenarios. In this paper, we present a generic framework for co-ordinating I/O and computation for geospatial simulations in a distributed computing environment. We present three parallel approaches and demonstrate the performance and scalability benefits of our parallel implementation pFUTURES, an extension of the FUTURES open-source multi-level urban growth model. Our analysis shows that although a time synchronous parallel approach obtains the same results as a sequential model, an asynchronous parallel approach provides better scaling due to reduced disk I/O and communication overheads.

Guest editorial: big spatial data

We are living in the era of ‘Big Data.’ Big data is currently the hottest topic for data researchers and scientists with huge interests from the industry and federal agencies alike, as evident in the recent White House initiative on BBig data research and development^. Big data, often characterized in terms of volume, velocity, variety, and veracity, is impacting the traditional data storage and processing frameworks. As the data sets are becoming large and complex, big data is posing challenges to traditional data storage and processing workflows including but not limited to data capture, transfer, storage, curation, search, query, analysis, and visualization. Spatial and spatiotemporal data, whether captured through remote sensors (e.g., remote sensing imagery, Atmospheric Radiation Measurement (ARM) data) or large scale simulations (e.g., climate data) has always been ‘Big.’ However, recent advances in instrumentation and computation, and advent of social media is making the spatiotemporal data even bigger, putting several constraints on data analytics capabilities. For example, Google generates more than 25 PB of data per day, a significant portion of which is spatiotemporal (images and videos) data. The rate at which spatiotemporal data is being generated clearly exceeds our ability to organize and analyze them to extract patters critical for understanding dynamically changing world. Spatial computation needs to be transformed to meet the challenges posed by the big spatial and spatiotemporal data. The purpose of this special issue is to showcase some of the recent developments and novel applications of the big spatial data field. The open call for big spatial data has attracted eleven papers covering broad range of spatial big data technologies and applications. After two rounds of peer-reviews by a team of international experts, four papers were selected to be included in this special issue. Geoinformatica (2016) 20:797–799 DOI 10.1007/s10707-016-0269-7