Ziheng Sun

GeoPW: Laying Blocks for the Geospatial Processing Web

Recent advances in Web-related technologies have significantly promoted the wide sharing and integrated analysis of distributed geospatial data. Geospatial applications often involve diverse sources of data and complex geoprocessing functions. Existing Web-based GIS focuses more on access to distributed geospatial data. In scientific problem solving, the ability to carry out geospatial analysis is essential to geoscientific discovery. This article presents the design and implementation of

Regular Shape Similarity Index: A Novel Index for Accurate Extraction of Regular Objects From Remote Sensing Images

It still remains a big challenge to accurately identify the geospatial objects with well-regulated outlines within remote sensing (RS) images such as residential buildings, factory storage buildings, highways, local roads, cars, and planes. In this paper, a novel spatial feature index, which is named regular shape similarity index (RSSI), is defined to address the challenge. It represents the ratio between the area of an object and its minimum bounding shape area. The application of RSSI in identifying objects with different shapes is discussed, and its capability is found to be a great supplement to the existing spatial feature hierarchy. An approach combining RSSI with object-based image analysis (OBIA) technology is proposed for image object extraction. A Web service for RSSI calculation is developed and integrated into a Web OBIA system. In the system, four experiments extracting factory storage buildings, residential buildings, roads, and planes, respectively, are conducted on three large-scale high-resolution RS images. In each experiment, two tests, i.e., one using traditional spatial features and the other using RSSI, are performed and compared. The results show that RSSI improves the accuracy of regular object extraction.

A Task Ontology Driven Approach for Live Geoprocessing in a Service-Oriented Environment

In a service-oriented environment, Web geoprocessing services can provide geoprocessing functions for a variety of applications including Sensor Web. Connecting Sensor Web and geoprocessing services together shows great potentail to support live geoprocessing using real-time data inputs. This article proposes a task ontology driven approach to live geoprocessing. The task in the ontology contains five aspects: task type, task priority, task constraints, task model, and task process. The use of the task ontology in driving live geoprocessing includes the following steps: (1) Task model generation, which generates a concrete process model to fulfill user demands; (2) Process model instantiation, which transforms the process model into an executable workflow; (3) Workflow execution: the workflow engine executes the workflow to generate value-added data products using Sensor Web data as inputs. The approach not only helps create semantically correct connections between Sensor Web and Web geoprocessing services, but also provides sharable problem

Agent-as-a-service-based geospatial service aggregation in the cloud

An Agent-as-a-Service (AaaS)-based geospatial service aggregation is proposed to build a more efficient, robust and intelligent geospatial service system in the Cloud for flood emergency response. It involves an AaaS infrastructure, encompassing the mechanisms and algorithms for geospatial Web Processing Service (WPS) generation, geoprocessing and aggregation. The method has the following advantages: 1) it allows separately hosted services and data to work together, avoiding transfers of large volumes of spatial data over the network; 2) it enriches geospatial service resources in the distributed environment by utilizing the agent cloning, migration and service regeneration capabilities of the AaaS, solving issues associated with lack of geospatial services to a certain extent; 3) it enables the migration of services to target nodes to finish a task, strengthening decentralization and enhancing the robustness of geospatial service aggregation; and 4) it helps domain experts and authorities solve interdisciplinary emergency issues using various Agent-generated geospatial services. Display Omitted Agent-as-a-Service (AaaS)-based geospatial service aggregation on the Cloud is proposed.It allows separately-hosted services and data to work together, which avoids transferring large volume of spatial data.It enriches geospatial service resources in the distributed environment and solves the issue of lack of geospatial services.It strengthens decentralization and enhances robustness of the geospatial service aggregation.It provides experts assistance in solving the interdisciplinary emergency issues with agent-generated geospatial services.

Automation of Customized and Near-Real-Time Vegetation Condition Index Generation Through Cyberinfrastructure-Based Geoprocessing Workflows

Vegetation condition index (VCI) is a popular index widely used in vegetation-related analyses. Traditional VCI products are fixed on temporal extent and unchangeable for common users. Customized VCI products, which contain a lot of new information that is unavailable in traditional VCI products, are usually difficult to generate. A new method to automate the generation of customized and near-real-time (NRT) VCI products is in great demand but remains a big challenge. Recent advances in Cyberinfrastructure (CI) have shown promise to meet this challenge. This paper presents a new approach using CI-based geoprocessing workflows to automatically generate VCI products at customized and NRT conditions. The utility of customized NRT VCIs in agriculture-related analyses is also discussed. The approach has been implemented as a new function in the Global Agricultural Drought Monitoring and Forecasting System (GADMFS). The experiment results demonstrate the feasibility of the proposed approach in facilitating the customization of VCI products and accelerating the generation of VCI products into NRT level.

Ontology-supported complex feature discovery in a web service environment

Web service technologies have shown great promise for rapid feature discovery from large volumes of remote sensing images. A complex feature is spatially composed of elementary features. The spatial relationships among elementary features can be used to discover complex features. This paper presents an ontology supported approach for detection task of complex features. The characteristic spatial relations for complex features are formalized and used for task modeling. Tasks are mapped to service ontologies to facilitate the creation of service chains. A prototype system is developed to demonstrate the applicability of the approach.

A provenance framework for Web geoprocessing workflows

In a service-oriented geoscientific research environment, individual geospatial services must be chained together as Web geoprocessing workflows to solve a complex geoscientific problem. The development of Web geoprocessing workflows can be divided into three phases: process modeling, process model instantiation, workflow execution. Provenance, or called lineage, records the derivation history of a data product. This paper presents a provenance framework for Web geoprocessing workflows. Such a framework includes the provenance representation, provenance recording, provenance storage, provenance service, and provenance applications. The concept of “three levels of geospatial provenance” is used to advocate the categories of provenance at the knowledge, service, and data level. The three-level view addresses the derivation history in the three-phase development of Web geoprocessing workflows. The applications of provenance are demonstrated by allowing re-orchestration of geoprocessing workflows at different phases using different levels of provenance and creating a more flexible system for Web geoprocessing workflows.

CyberConnector: a service-oriented system for automatically tailoring multisource Earth observation data to feed Earth science models

Feeding multisource Earth observation (EO) data into Earth science models (ESM) remains a daunting challenge. This paper presents a service-oriented approach as an alternative solution. It uses geospatial web services to process the EO data and geoprocessing workflow for automation. Different from existing approaches, it takes advantage of virtual data products (VDP) to release modelers from intensive data processing. It can directly connect ESMs to public EO sources via Cyberinfrastructure. A prototype called CyberConnector is implemented. CyberConnector supports intuitive building of VDP, automatic execution of workflows and effortless retrieval of model-ready input files. We used it to stream multiple datasets to several ESMs including finite-volume coastal ocean model (FVCOM) and cloud-resolving model (CRM). The results show that CyberConnector can truly benefit modelers on time saving and effort minimizing.

GeoFairy: Towards a one-stop and location based Service for Geospatial Information Retrieval

Abstract It is still a great challenge to efficiently deliver dynamic and heterogeneous Earth observation (EO) information to users based on their real time locations. However, the rapidly evolving techniques create a chance to meet the challenge. This paper proposes a framework to realize a one-stop and location based service (LBS) for geospatial information (GI) retrieval on mobile platforms. The framework originally integrates a number of state-of-the-art techniques with geospatial data resources and let them cooperate together to provide a robust and highly available LBS. Cloud platform is used to deploy the server module. A location enabled load balancing algorithm is presented to balance the cloud instance VMs on behalf of LBS. A system named GeoFairy is implemented. It provides a one-stop service for gathering and delivering twelve kinds of GI on real time locations. Two Apps are built for the major mobile ecosystems: iOS and Android. Many tests, including a stress test, have been made via a number of mobile devices at various locations. The results demonstrate that GeoFairy is capable of one-stop delivering real-time GI to users and significantly reducing costs on information searching and retrieving. This feature is very helpful in many scenarios such as disaster responding and military actions. This research paves a way on both theoretical and practical aspects for researchers and developers to realize operational mobile applications for one stop and location based GI retrieval.

Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image classification activities. Currently, the approach is used only on high resolution optical three-band remote sensing imagery. The feasibility using the approach on other kinds of remote sensing images or involving additional bands in classification will be studied in future.