A CyberGIS Integration and Computation Framework for High‐Resolution Continental‐Scale Flood Inundation Mapping

We present a Digital Elevation Model (DEM)-based hydrologic analysis methodology for continental flood inundation mapping (CFIM), implemented as a cyberGIS scientific workflow in which a 1/3rd arc-second (10m) Height Above Nearest Drainage (HAND) raster data for the conterminous U.S. (CONUS) was computed and employed for subsequent inundation mapping. A cyberGIS framework was developed to enable spatiotemporal integration and scalable computing of the entire inundation mapping process on a hybrid supercomputing architecture. The first 1/3rd arc-second CONUS HAND raster dataset was computed in 1.5 days on the CyberGIS ROGER supercomputer. The inundation mapping process developed in our exploratory study couples HAND with National Water Model (NWM) forecast data to enable near real-time inundation forecasts for CONUS. The computational performance of HAND and the inundation mapping process was profiled to gain insights into the computational characteristics in high-performance parallel computing scenarios. The establishment of the CFIM computational framework has broad and significant research implications that may lead to further development and improvement of flood inundation mapping methodologies

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach

A distributed workload-aware approach to partitioning geospatial big data for cybergis analytics

Numerous applications and scientific domains have contributed to tremendous growth of geospatial data during the past several decades. To resolve the volume and velocity of such big data, distributed system approaches have been extensively studied to partition data for scalable analytics and associated applications. However, previous work on partitioning large geospatial data focuses on bulk-ingestion and static partitioning, hence is unable to handle dynamic variability in both data and computation that are particularly common for streaming data. To eliminate this limitation, this thesis holistically addresses computational intensity and dynamic data workload to achieve optimal data partitioning for scalable geospatial applications. Specifically, novel data partitioning algorithms have been developed to support scalable geospatial and temporal data management with new data models designed to represent dynamic data workload. Optimal partitions are realized by formulating a fine-grain spatial optimization problem that is solved using an evolutionary algorithm with spatially explicit operations. As an overarching approach to integrating the algorithms, data models and spatial optimization problem solving, GeoBalance is established as a workload-aware framework for supporting scalable cyberGIS (i.e. geographic information science and systems based on advanced cyberinfrastructure) analytics

From SpaceStat to CyberGIS: Twenty Years of Spatial Data Analysis Software

This essay assesses the evolution of the way in which spatial data analytical methods have been incorporated into software tools over the past two decades. It is part retrospective and prospective, going beyond a historical review to outline some ideas about important factors that drove the software development, such as methodological advances, the open source movement and the advent of the internet and cyberinfrastructure. The review highlights activities carried out by the author and his collaborators and uses SpaceStat, GeoDa, PySAL and recent spatial analytical web services developed at the ASU GeoDa Center as illustrative examples. It outlines a vision for a spatial econometrics workbench as an example of the incorporation of spatial analytical functionality in a cyberGIS.

Cloud computing based bushfire prediction for cyber-physical emergency applications

In the past few years, several studies proposed to reduce the impact of bushfires by mapping their occurrences and spread. Most of these prediction/mapping tools and models were designed to run either on a single local machine or a High performance cluster, neither of which can scale with users' needs. The process of installing these tools and models their configuration can itself be a tedious and time consuming process. Thus making them, not suitable for time constraint cyber-physical emergency systems. In this research, to improve the efficiency of the fire prediction process and make this service available to several users in a scalable and cost-effective manner, we propose a scalable Cloud based bushfire prediction framework, which allows forecasting of the probability of fire occurrences in different regions of interest. The framework automates the process of selecting particular bushfire models for specific regions and scheduling users' requests within their specified deadlines. The evaluation results show that our Cloud based bushfire prediction system can scale resources and meet user requirements. © 2017 Elsevier B.V

A Secure Data Enclave and Analytics Platform for Social Scientists

Data-driven research is increasingly ubiquitous and data itself is a defining asset for researchers, particularly in the computational social sciences and humanities. Entire careers and research communities are built around valuable, proprietary or sensitive datasets. However, many existing computation resources fail to support secure and cost-effective storage of data while also enabling secure and flexible analysis of the data. To address these needs we present CLOUD KOTTA, a cloud-based architecture for the secure management and analysis of social science data. CLOUD KOTTA leverages reliable, secure, and scalable cloud resources to deliver capabilities to users, and removes the need for users to manage complicated infrastructure.CLOUD KOTTA implements automated, cost-aware models for efficiently provisioning tiered storage and automatically scaled compute resources.CLOUD KOTTA has been used in production for several months and currently manages approximately 10TB of data and has been used to process more than 5TB of data with over 75,000 CPU hours. It has been used for a broad variety of text analysis workflows, matrix factorization, and various machine learning algorithms, and more broadly, it supports fast, secure and cost-effective research