FIELD GROUND TRUTHING DATA COLLECTOR -- A MOBILE
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-4, 2012XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, AustraliaFIELD GROUND TRUTHING DATA COLLECTOR –A MOBILE TOOLKIT FOR IMAGE ANALYSIS AND PROCESSINGX. MengWuhan University, No. 37, Luoyu Road, Wuhan, Hubei Province 430079, China – xmeng@whu.edu.cnCommission IV, IV/5KEY WORDS: Mobile Mapping, Web Services, Spatial Query, Image Analysis and Processing, VisualizationABSTRACT:Field Ground Truthing Data Collector is one of the four key components of the NASA funded ICCaRS project, being developedin Southeast Michigan. The ICCaRS ground truthing toolkit entertains comprehensive functions: 1) Field functions, includingdetermining locations through GPS, gathering and geo-referencing visual data, laying out ground control points for AEROKATflights, measuring the flight distance and height, and entering observations of land cover (and use) and health conditions ofecosystems and environments in the vicinity of the flight field; 2) Server synchronization functions, such as, downloadingstudy-area maps, aerial photos and satellite images, uploading and synchronizing field-collected data with the distributeddatabases, calling the geospatial web services on the server side to conduct spatial querying, image analysis and processing, andreceiving the processed results in field for near-real-time validation; and 3) Social network communication functions for directtechnical assistance and pedagogical support, e.g., having video-conference calls in field with the supporting educators, scientists,and technologists, participating in Webinars, or engaging discussions with other-learning portals. This customized softwarepackage is being built on Apple iPhone/iPad and Google Maps/Earth. The technical infrastructures, data models, couplingmethods between distributed geospatial data processing and field data collector tools, remote communication interfaces, codingschema, and functional flow charts will be illustrated and explained at the presentation. A pilot case study will be alsodemonstrated.ICCaRS ground truthing toolkit entertains comprehensivefunctions, including Field Function, Server SynchronizationFunctions and Social Network Communication Functions. Inthis study, the customized software package is being built onApple iPhone/iPad and Google Maps/Earth.1. INTRODUCTIONThe NASA funded ICCaRS (Investigating Climate Changeand Remote Sensing) Project, is being developed in SoutheastMichigan by Eastern Michigan University and WayneCounty Regional Educational Service Agency. The four keycomponents of the project are the NASA AEROKATSTwinCam-AeroPod Field Operation Manual and the ImageProcessing Lab Guide, ICCaRS NASA STEM InstructionalUnits, ICCaRS eLearning Collaboratory and the FieldGround Truthing Data Collector. The Collaboratory is aneLearning portal which includes a web-based geographicinformation mapping system, an field observation databaseand group-ware for social networking to build the supportingcommunity. The goal of ICCaRS is that K9-12 sudents andteachers will have a working understanding of the sciencebehind global climate change and its relationship to land-useand land-cover (LULC) changes on multiple scales throughNASA data products and models. Students and teachers willlearn how remotely sensed data can be used to study thephenomena of global climate change on multiple levels (i.e.,scales of size: local, regional and global), be able to acquireremotely sensed data and produce meaningful information(including vegetation index, biomass and LULC change overtime) from that data. The Field Ground Truthing DataCollector is focussed on the purpose, through serving as fieldcommunication portal to transmit field data to server, and asmobile toolkit for image analysis and processing.The remainder of the paper is organized as follows: Section 2gives the comparison of the current top-end handheld mobilephones to illustrate the reason for choosing iPhone/iPad asthe handheld platform, while Section 3 describes thetechnical infrastructures of the ICCaRS eLearningCollaboratory web-portal as well as the handheld groundtruthing data collector. Section 4 gives the four primary datamodels in the ICCaRS project. Section 5 and 6 describe theremote communication interfaces and the coupling methodsbetween image analysis and field data collector tools. InSection 7, the comprehensive functions of the case study aredemonstrated. Finally, Section 8 concludes the paper anddiscusses future work.2. COMPARISON OF TOP-END HANDHELDMOBILE PHONESIn what follows, we present a short qualitative comparison ofthe Google Nexus One, Nokia N900, Apple iPhone 4 andiPad 2. All these devices are actively being used in support ofmobile sensing applications and systems development. TheGoogle Nexus One is the flagship Android device launchedby Google and HTC, while the Nokia N900 is currently oneof the top-end Nokia mobile phones. Both mobile operatingsystems, Android and Maemo, are open source software, andcan be liberally extended to incorporate new cutting edgetechnologies as they emerge. However, there’s no doubt thatSince providing current mobile phones with more sensingcapabilities would greatly enhance the humans presenceclassification accuracy given the broader input to theclassifiers feature vectors (Miluzzo, 2008), the developing157
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-4, 2012XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australiain the world of technological devices, 2011 was a great yearfor Apple’s products, the iPhone and iPad.Cellular: All cell tower locations are known precisely, so ifone handheld mobile is communicating with either GSM or3G, the mobile can pinpoint location to roughly 1500 meters.Normally, several towers can be detected, so the first roughlocation can be obtained and calculated in 10 seconds or less.This calculated data is most times less precise than waitingfor satellite data.A simple comparison of some of the technical details of thefour handheld devices is reported in Table 1. As shown inTable 1, all four platforms present similar computationalcapabilities given similar processors, large storage andmemory size. The screen size of iPad is much bigger than theNexus One, N900 and iPhone, which is much better for“expressing the software” (Bosker, 2010), especially forexpressing mobile mapping applications, photos and images.However, the 9.7 inch screen makes the weight of iPadheavier than the other three. The iPad also has cameras atfront and backside. Since taking photos are very importantfor field data collection, built-in cameras become one of thepeople-centric features.WiFi: WiFi data are used to obtain position. Although not asaccurate as GPS technology, indoor locations can be obtainedadditionally to outdoor positions. Apple built a database,leveraging its large base of users to log basic WiFi data. Thetechnology used here is called WiFi Positioning System(WPS). The database stores the MAC of the WiFi accesspoint you are connected to and links this MAC to a location.Most WiFi signals work within 20 meters (with walls) to 200meters (no obstacles), but the accuracy also dependent onwhether the location was entered into the database correctly.Both iPhone and iPad run the same operating system, iOS,originally developed for the iPhone. The specialties of thedevelopment of iPhone/iPad applications are illustrated in thenext section. Because the operating systems are the same, alliPhone applications are compatible with iPad. The mobiletoolkits developed for ICCaRS are for both handheld devices.Digital Compass: It provides directional information thatGPS can't. Digital compass is also helpful for coupling thelocal scale measurements with landscape scale remotesensing data such as satellite or the high altitude d.Broadcom BCM4750UBG, which iPhone and iPad use as thecore chipset, is a single-chip GPS solution. This chipsets isdesigned for Assisted GPS (A-GPS or aGPS) solution whichcan reduce the time-to-first-fix (TTFF) and possibly increasethe sensitivity even further (Jarvinen 2002). Normally,standalone GPS must search for satellite signals in a clearview of the sky, but A-GPS additionally uses networkresources to locate and utilize the satellites faster even whenthe receiver is located in poor signal conditions.Because the technologies and models are different, theperformance of these “high sensitivity” GPS (HSGPS)receivers in terms of TTFF and accuracy does vary [5]. Manyefforts have been reported in the literature on evaluation ofhandheld devices (Miluzzo, 2008, Thiagarajan, 2010, Zhang,2010). It is generalized that all those GPS receiversperformed well in urban-like environment or indoors, and thecomputational capability of the iPhone/iPad is sufficient tohandle high load fast fourier transform (FFT) calculations.Since the iPhone/iPad can provide a good position, velocityand time (PVT) solution, the customized software packagesof ICCaRS project are built on them.Besides A-GPS, the common technologies which can be usedfor receiving location information in the Field GroundTruthing Data Collector are:DeviceReleaseYear2010OperatingSystemAndroid 2NokiaN9002009AppleiPhone 4AppleiPad 1 GHzQualcommQSD 8250Snapdragon512 MBDRAM3.7 in (9.4cm) diagonal,480 x 800 px5.0 MPBackside0.29 lb(130 g)Maemo 5600 MHzTI OMAP34303.5 in (8.9cm) diagonal,800 480 px5.0 MPBackside0.3 MPFront0.4 lb(181 g)2010iOS 41 GHzApple A4256 MBMobileDDR,768 MBswap space512 MBeDRAM512 MBFlashmemory,microSD,expandableup to 32 GB256 MBNAND flash,32 GB eMMCflash16 or32 GB,Flash memory3.5 in (8.9cm) diagonal,960 640 px0.3 lb(137 g)2011iOS 41 GHzApple A4256 MBDRAM16, 32, or64 GB,Flash memory9.7 in (25 cm)diagonal,1024 768 px5.0 MPBackside3.0 MPFront5.0 MPBackside3.0 MPFrontTable 1 Platform Comparison for the Google Nexus One, Nokia N900 and Apple iPhone/iPad1581.6 lb(730 g)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-4, 2012XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australiadevelopers can only use the Apple-defined public APIs. TheiPhone/iPad indeed has drawbacks but still has excellentsupport for HTML5 web applications with its Safari browserusing WebKit engine. This specialty is good for realizing afast web-based mobile mapping. It should be also noted thatiOS 4 does not fully support multitasking. It supportsbackground threads for tasks such as music playback andnetwork polling. All other application threads are frozenwhen the application is deactivated (Dorokhova, 2009).3. TECHNICAL INFRASTRUCTURESFigure 1 illustrates the technical infrastructures of theweb-portal (eLearning Collaboratory) as well as handhelddevice (Field Ground Truthing Data Collector). The currenttechnical infrastructures build on the preliminary work, alsoby Eastern Michigan University and Wayne County RegionalEducational Service Agency (RESA), who developed aweb-based GIS/database in the RESA H2OMapper which ispiloted as part of a watershed program targeting middle andearly high school learners (Meng, 2009).4. DATA MODELSFollowing are four primary data models in the ICCaRSproject. The four models are divided according to functionalneeds, but mutual overlapping.1.User-Roles Data Model: representing theuser-roles relationship2.Platform Routine Data Model: representing theroutine functions of eLearning Collaboratory andField Ground Truthing Data Collector3.Geospatial Data Model: representing thegeospatial information retrieved through webservices4.Ground Truthing Data Model: representing thecombination of various data collectionsFigure 1. Technical Infrastructures of Web-portal as well asthe HandheldOn the server side, the Apache HTTP Server are installed andconfigured for providing web services including geospatialinformation services. The server holds PostgreSQL as thedatabase integrated with PostGIS which supports spatial datamanagement, and save the images. The Collaboratory usesPHP as scripting language to develop the Web 2.0 sites.On the developer side, EclipsePHP and YUI are two usefulopen source tools for PHP development. The package ofXcode and iOS SDK can be downloaded from Apple website,and includes the Xcode IDE, iOS Simulator, and a suite ofadditional tools for developing apps for iPhone and iPad.On the user (test) side, the eLearning Collaboratory worksbest with the browsers which support Web 2.0, such asChrome 6 , Firefox 3.6 , Safari 5 or Opera 10 .The Google Earth plug-in allows users to navigate andexplore geographic data on a 3D globe using a web browserthrough retrieving the geospatial data from web services.However, so far, it cannot work with Apple handheld devices.In order to make the web-based GIS of eLearningCollaboratory working, a toolkit for converting Google Earthplug-in maps to Google Maps which can work withiPhone/iPad is also being developed.Figure 2. Ground Truthing Data ModelFigure 2 shows the relationship among key data entities ofGround Truthing Data Model. For instance, School A does aninvestigation, which name is Investigation A, at a Study Site.School A will get the flight mission data by means of flyingthe NASA AEROKATS TwinCam-AeroPod. Also School Awill do some observations in this area. Some observationpoint could be used as ground control point (GCP). Besidesthe observation points, some identifiable point could be alsoused as GCPs. Tabular data entry forms on different areas,such as clouds, soils, temperatures and etc., are prepared forfield data collection at the points and regions of interest.The iPhone/iPad applications are written in Objective-C.There are yearly costs to be registered to develop on theplatform and publish the application. Not only that,159
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-4, 2012XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, AustraliaThere exists a need for publishing, sharing, and accessingspatial data, which are AEROKATS TwinCam-AeroPodimages, from distributed geospatial databases interconnectedthrough web-based geo-information services. The technicalproblems can be induced to three main aspects: (1) Findinglocalization of the required images, (2) Enabling remoteaccess to the images, and (3) Transforming different imagerepresentations. In order to solve the technical challenges,the conformance to standards is essential for distributedgeospatial database systems. OGC Web Processing Servicestandards and use of web services chaining (Meng 2010)could offer the solution for the above issues, and make thedistributed image analysis and processing feasible.5. REMOTE COMMUNICATION INTERFACESFrom the early mobile devices which always only supportedGSM interfaces, to the current iPhone and Andriod rangesthat incorporate numerous connectivity interfaces such as 3G,WiFi, Bluetooth and GPS, handheld devices become muchmore powerful. WiFi or 3G connectivity enables developersto create intuitive and function rich mobile applicationscapable of connecting to external services hosted around theworld. Coupling GPS with WiFi and 3G enabled devicesyield an even greater potential for integration and innovation.The Field Ground Truthing Data Collector is a proposedcollaboration toolkit that is created by a framework ofcomponents utilizing GPS, Internet connectivity and webservices. It communicates in sync to provide client users witha set of platform and device independent tracking andcollaboration features, which are possible by exploiting theinterfaces available to the device. If a particular user isconnected to a WiFi hotspot, the mobile device operatingsystem is smart enough to determine the best way ofconnecting to the content requested by the user. If WiFiconnectivity is not available, the device attempts to fetch thesame content by other means of communication, usuallyinitiating a 3G data connection (Savic 2010).Web services for data synchronizing, geo-position, spatialquery, image analysis and processing are defined in an XMLformat that describes the network services as set of endpointsor ports. In WSDL, the abstract definition of endpoints andmessages (functions and functions parameters) are separatedfrom their concrete network deployment or data formatbindings. The core components of the mobile data collectortoolkit are the web services defined by the WSDL definition.Further looking into the “updatePosition” operation which isfor geo-position, a detailed code definition is defined below: webServer new soap server(); namespace t/services/ groundTruthing.php?wsdl'; webServer- configureWSDL('groundTruthingServices'); webServer- wsdl- schemaTargetNamespace namespace; soapAction false; style 'rpc'; use 'encoded'; methodName 'updatePosition'; description 'Update position method. Automatically calledwhen longatude and latitude position is updated.'; input array('longitude' 'xsd:string','latitude' 'xsd:string', 'token' 'xsd:string','trackid' 'xsd:int'); output array('return' 'xsd:string'); webServer- register( methodName, input, output, namespace, soapAction, style, use, description);Figure 3. The images in the same view field, and theprocessed result which is conducted by MultiSpecBy coupling digital aerial and ground photos, A-GPS, fielddata collected with the mobile devices and web servicesapplied in the eLearning Collaboratory server enabled de/longitude) of investigation site with vegetation,watershed and soil parameters. In other words, coupling ourlocal scale measurements with landscape scale remotesensing data such as satellite and the high altitude aerialphotography which is taken by the NASA AEROKATSTwinCam-AeroPod, we can have a complete picture ofland-use and land-cover changes on multiple scales.6. COUPLING METHODS BETWEEN IMAGEANALYSIS AND FIELD DATA COLLECTOR TOOLSRemote sensing data, in conjunction with image analysissoftware, are being used to quantify vegetation cover(Louhaichi, 2010). Recently, we have two cameras: one fornear infrared (NIR) video and the other for visible bandsvideo. We are searching for good quality of paired images:one NIR image matching with another visible-band image inSpatial analysis (or process) which includes spatial query,feature and image analysis using the technology ofdistributed geospatial database systems becomes more andmore important because of its capacity of supporting datainteroperability in transmission (Ramirez 2001).160
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-4, 2012XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australiaterms of exactly the same view field. After exporting thevideo files taken by the NASA AEROKATS TwinCamAeroPod, a large number of images are generated. But only asmall percentage of them have good quality and can be usedto find “matching pairs.” Figure 3 shows the images in thesame view field, and the processed result which is conductedby MultiSpec. The “Training Class Performance” tabletabulates how well the pixels of each field and the classeswere classified. If the Reference Accuracy is particularly low(say less than 50%) for a class, the training pixels for thatclass should be reexamined and new training pixels should beselected. The higher of the Overall Class Performance, thebetter of the classification result.7. GROUND TRUTHING TOOLKIT FUNCTIONSThe ICCaRS field ground truthing toolkit entertainscomprehensive functions, such as field functions, serversynchronization functions and social network communicationfunctions, for image analysis and processing.Figure 4. User Interface of the ICCaRS Field GroundTruthing Data Collector7.1 Field FunctionsFigure 5 illustrates the functional flow chart of serversynchronization and related web services. The authorizedclients who hold the iPhone/iPad have the ability to submitthe data to the server side through “Data Submitting Service”and “Image Submitting Service”. The Client also needs tocall the Server for retrieving the image-processed resultsthrough “Image Analysis Retrieving Service”. There is astack used as “Download Warehouse” storing the status ofthe image as well as the attribute forms, and the status showswhether the images are ready for downloading or not. “StatusChecking Service” is used for checking the “ready” status.The ICCaRS ground truthing toolkit is a kind of GISvisualization. It has graphical software interface which makesit easy to learn and simple operated. It is being developed toprovide various position data and attribute informationcollection methods, such as manual collection, automaticcollection, public point collection, offset collection, manualinputting and menu selection. Figure 4 represents the userinterface of the ICCaRS field ground truthing data collector.Furthermore, internal microphone is used for real time voiceinformation for marking attributes. Field functions of theICCaRS ground truthing toolkit include:1.2.3.4.5.7.3 Social Network Communication Functions for DirectTechnical Assistance and Pedagogical SupportDetermining locations through A-GPS whichwas illustrated in Section 2Gathering and geo-referencing audio andvisual dataLaying out ground control points forAEROKAT flightsMeasuring the flight distance and heightEntering observations of land cover (and use)and health conditions of ecosystems andenvironments in the vicinity of the flight fieldSince the ICCaRS field ground truthing data collector usesthe iPhone/iPad platform, it can provide users the originalfunctions, like browsing the web, enjoying photos and videos,books, periodicals and music, taking notes, emailing, doingsocial networking, participating in Webinars, and engagingdiscussions with other-learning portals. Thousands of Appleapplications can be found from the App store. Almost allcurrent social networking applications have launched aniPhone and/or iPad application.The ICCaRS project is coupling with social networks,Facebook, Twitter, Google Picasa and Blogger. These socialapplications are embedded into a user’s workspace (as shownin Figure 6) of the Collaboratory portal. Users can use theirworkspaces when they are doing field observations, if theyhave 3G connection. Video-conference calls in field with thesupporting educators, scientists, and technologists,participating in Webinars, or engaging discussions withother-leaning portals. Users can also use the iPhone/iPad tohave video calls, with the assistance of a FaceTimeapplication, to another iPhone/iPad.7.2 Server Synchronization FunctionsThe handheld device is able to real time update data to serveron study site, and achieves real time management throughweb services which are deployed in the Apache server side.Server synchronization functions of the ICCaRS groundtruthing toolkit include:1.2.3.4.Uploading and synchronizing field-collecteddata with the main database on the serverCalling the tools on the server to conductimage analysis and processingReceiving the image-processed results in fieldfor near-real-time validationDownloading study-area maps, aerial photosand satellite images8. CONCLUSIONSThis paper outlines how to couple handheld devices,geo-referenced AEROKATS TwinCam AeroPod photos and161
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-4, 2012XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australiaweb services in order to achieve image analysis andprocessing. A mobile toolkit is developed for the field groundtruthing data collector. A prototype web platform fordeploying the services is also developed in the ICCaRSeLearning Collaboratory portal. The future work of our studywill concentrate on realizing more sophisticated distributedgeoinformation services for image analyses and processing,particularly on the remote accessing and manipulation oflarge geo-referenced images. Some applications, like usingNormalized Difference Vegetation Indices (NDVI) data tocompute biomass and predict yield, will be considered to beput into the mobile toolkit.Figure 5. Functional Flow Chart of Server Synchronizationrangeland vegetation cover at local scale. InternationalJournal of Agriculture & Biology, 12, pp. 406–410.Meng, X., Bian, F., Xie, Y., 2009. The Application ofWeb-based GIS and GPS Technology to Assess WaterQuality in Michigan. In: The 3rd International Conference onIntelligent Information Technology Application, Volume 1,pp. 517-520.Meng, X., Xie, Y., Bian, F., 2010. Distributed GeospatialAnalysis through Web Processing Service: A Case Study ofEarthquake Disaster Assessment. Journal of Software, 5(6),pp. 671-679.Figure 6. User’s WorkspaceMiluzzo, E., Oakley, J., Lu, H., Lane N., Peterson, R.,Campbell, A., 2008, Evaluating the iPhone as a mobileplatform for people-centric sensing applications. In Proc.of Intl Workshop on Urban, Community, and SocialApplications of Networked Sensing Systems (UrbanSense08).Raleigh, NC, USA, pp. 41-45.REFERENCESBosker, B., 2010. “Apple's iPad 2 Won't Be A Smaller,7-Inch Version, Steve Jobs Suggests”. The Huffington es-ipad-2-wont-be-a-s n 767882.html (7 Jan. 2011).Ramirez, M., 2001. Distributed Processing of Spatial Queries.Tese Dsc, COPPE/UFRJ.Dorokhova, R., Amelichev, N., Krinkin, K., 2009.“Evaluation of Modern Mobile Platforms from the nts/download/427/evaluation edit 1.doc (11 Jan. 2011).Savic, S., Shi, H., 2010. TEAMTRACKER - An InnovativeTeam Collaboration System. International Journal ofComputer Networks & Communications (IJCNC) 2(5), pp.105-118Jarvinen, J., DeSalas, J., LaMance, J., 2002. “Assisted cture-approach-734 (11 Jun. 2008).Thiagarajan, A., Biagioni, J., Gerlich, T., Eriksson, J., 2010.Cooperative Transit Tracking using Smart-phones. In:SenSys '10 Proceedings of the 8th ACM Conference onEmbedded Networked Sensor Systems.Louhaichi, M., Johnson, M., Woerz, A., Jasra, A., andJohnson, D., 2010. Digital charting technique for monitoringZhang, J., Binghao Li, B., Dempster, A., Rizos, C., 2010.Evaluation of High Sensitivity GPS Receivers. InternationalSymposium on GPS/GNSS Taipei, Taiwan, pp. 1-6162
the Google Nexus One, Nokia N900, Apple iPhone 4 and iPad 2. All these devices are actively being used in support of mobile sensing applications and systems development. The Google Nexus One is the flagship Android device launched by Google and HTC, while the Nokia N900 is currently one of the top-end
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