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Dive into the research topics where Inian Moorthy is active.

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Featured researches published by Inian Moorthy.


Scientific Data | 2017

A global dataset of crowdsourced land cover and land use reference data

Steffen Fritz; Linda See; Christoph Perger; Ian McCallum; C. Schill; D. Schepaschenko; Martina Duerauer; Mathias Karner; C. Dresel; Juan-Carlos Laso-Bayas; M. Lesiv; Inian Moorthy; Carl F. Salk; O. Danylo; Tobias Sturn; Franziska Albrecht; Liangzhi You; F. Kraxner; Michael Obersteiner

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general.


International Journal of Disaster Risk Science | 2016

Technologies to Support Community Flood Disaster Risk Reduction

Ian McCallum; Wei Liu; Linda See; R. Mechler; Adriana Keating; S. Hochrainer-Stigler; Junko Mochizuki; Steffen Fritz; Sumit Dugar; Miguel Arestegui; Michael Szoenyi; Juan-Carlos Laso Bayas; Peter Burek; Adam French; Inian Moorthy

Abstract Floods affect more people globally than any other type of natural hazard. Great potential exists for new technologies to support flood disaster risk reduction. In addition to existing expert-based data collection and analysis, direct input from communities and citizens across the globe may also be used to monitor, validate, and reduce flood risk. New technologies have already been proven to effectively aid in humanitarian response and recovery. However, while ex-ante technologies are increasingly utilized to collect information on exposure, efforts directed towards assessing and monitoring hazards and vulnerability remain limited. Hazard model validation and social vulnerability assessment deserve particular attention. New technologies offer great potential for engaging people and facilitating the coproduction of knowledge.


Remote Sensing | 2016

Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology

Juan Carlos Laso Bayas; Linda See; Steffen Fritz; Tobias Sturn; Christoph Perger; M. Dürauer; Mathias Karner; Inian Moorthy; D. Schepaschenko; D. Domian; Ian McCallum

Citizens are increasingly becoming involved in data collection, whether for scientific purposes, to carry out micro-tasks, or as part of a gamified, competitive application. In some cases, volunteered data collection overlaps with that of mapping agencies, e.g., the citizen-based mapping of features in OpenStreetMap. LUCAS (Land Use Cover Area frame Sample) is one source of authoritative in-situ data that are collected every three years across EU member countries by trained personnel at a considerable cost to taxpayers. This paper presents a mobile application called FotoQuest Austria, which involves citizens in the crowdsourcing of in-situ land cover and land use data, including at locations of LUCAS sample points in Austria. The results from a campaign run during the summer of 2015 suggest that land cover and land use can be crowdsourced using a simple protocol based on LUCAS. This has implications for remote sensing as this data stream represents a new source of potentially valuable information for the training and validation of land cover maps as well as for area estimation purposes. Although the most detailed and challenging classes were more difficult for untrained citizens to recognize, the agreement between the crowdsourced data and the LUCAS data for basic high level land cover and land use classes in homogeneous areas (ca. 80%) shows clear potential. Recommendations for how to further improve the quality of the crowdsourced data in the context of LUCAS are provided so that this source of data might one day be accurate enough for land cover mapping purposes.


Remote Sensing | 2017

LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya

Linda See; Juan Carlos Laso Bayas; D. Schepaschenko; Christoph Perger; C. Dresel; V. Maus; Carl F. Salk; Jürgen Weichselbaum; M. Lesiv; Ian McCallum; Inian Moorthy; Steffen Fritz

Accuracy assessment, also referred to as validation, is a key process in the workflow of developing a land cover map. To make this process open and transparent, we have developed a new online tool called LACO-Wiki, which encapsulates this process into a set of four simple steps including uploading a land cover map, creating a sample from the map, interpreting the sample with very high resolution satellite imagery and generating a report with accuracy measures. The aim of this paper is to present the main features of this new tool followed by an example of how it can be used for accuracy assessment of a land cover map. For the purpose of illustration, we have chosen GlobeLand30 for Kenya. Two different samples were interpreted by three individuals: one sample was provided by the GlobeLand30 team as part of their international efforts in validating GlobeLand30 with GEO (Group on Earth Observation) member states while a second sample was generated using LACO-Wiki. Using satellite imagery from Google Maps, Bing and Google Earth, the results show overall accuracies between 53% to 61%, which is lower than the global accuracy assessment of GlobeLand30 but may be reasonable given the complex landscapes found in Kenya. Statistical models were then fit to the data to determine what factors affect the agreement between the three interpreters such as the land cover class, the presence of very high resolution satellite imagery and the age of the image in relation to the baseline year for GlobeLand30 (2010). The results showed that all factors had a significant effect on the agreement.


Scientific Data | 2017

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

Juan Carlos Laso Bayas; M. Lesiv; François Waldner; Anne Schucknecht; Martina Duerauer; Linda See; Steffen Fritz; Dilek Fraisl; Inian Moorthy; Ian McCallum; Christoph Perger; O. Danylo; Pierre Defourny; Javier Gallego; Sven Gilliams; Ibrar ul Hassan Akhtar; Swarup Jyoti Baishya; Mrinal Baruah; Khangsembou Bungnamei; Alfredo Campos; Trishna Changkakati; Anna Cipriani; Krishna Das; Keemee Das; Inamani Das; Kyle Frankel Davis; Purabi Hazarika; Brian Alan Johnson; Ziga Malek; Monia Elisa Molinari

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.


GI_Forum | 2018

Using Citizen Science to Help Monitor Urban Landscape Changes and Drive Improvements

Karin Wannemacher; Barbara Birli; Tobias Sturn; Richard Stiles; Inian Moorthy; Linda See; Steffen Fritz

Citizen Science has become a vital source for data collection when the spatial and temporal extent of a project makes it too expensive to send experts into the field. However, involving citizens can go further than that – participatory projects focusing on subjective parameters can fill in the gap between local community needs and stakeholder approaches to tackle key social and environmental issues. LandSense, a Horizon 2020 project that is deeply rooted in environmental challenges and solutions, aims to establish a citizen observatory that will provide data to stakeholders, from researchers to businesses. Within this project, a mobile application has been developed that aims not only to stimulate civic engagement to monitor changes within the urban environment, but also to enable users to drive improvements by providing city planners with information about the public perception of urban spaces. The launch of a public version of such an app requires preparation and testing by focus groups. Recently, a prototype of the app was used by both staff and students from Vienna University of Technology, who contributed valuable insights to help enhance this citizen science tool for engaging and empowering the inhabitants of the city.


Agricultural Systems | 2018

A comparison of global agricultural monitoring systems and current gaps

Steffen Fritz; Linda See; Juan Carlos Laso Bayas; François Waldner; Damien Christophe Jacques; Inbal Becker-Reshef; Alyssa K. Whitcraft; Bettina Baruth; Rogerio Bonifacio; Jim Crutchfield; Felix Rembold; Oscar Rojas; Anne Schucknecht; Marijn van der Velde; James Verdin; Bingfang Wu; Nana Yan; Liangzhi You; Sven Gilliams; Sander Mücher; Robert Tetrault; Inian Moorthy; Ian McCallum


Archive | 2018

A global snapshot of the spatial and temporal distribution of very high resolution satellite imagery in Google Earth and Bing Maps as of 11th of January, 2017

M. Lesiv; Linda See; Juan-Carlos Laso-Bayas; Tobias Sturn; D. Schepaschenko; Mathias Karner; Inian Moorthy; Ian McCallum; Steffen Fritz


Archive | 2016

Assessing the quality of crowdsourced in-situ land-use and land cover data from FotoQuest Austria application

Juan-Carlos Laso-Bayas; Linda See; Steffen Fritz; Tobias Sturn; Mathias Karner; Christoph Perger; M. Dürauer; T. Mondel; D. Domian; Inian Moorthy; Ian McCallum; D. Shchepashchenko; F. Kraxner; Michael Obersteiner


international journal of spatial data infrastructures research, , | 2018

Engaging Citizens in Environmental Monitoring via Gaming

Ian McCallum; Linda See; Tobias Sturn; Carl F. Salk; Christoph Perger; M. Dürauer; Mathias Karner; Inian Moorthy; D. Domian; D. Schepaschenko; Steffen Fritz

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Linda See

International Institute for Applied Systems Analysis

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Steffen Fritz

International Institute for Applied Systems Analysis

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Ian McCallum

International Institute for Applied Systems Analysis

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Tobias Sturn

International Institute for Applied Systems Analysis

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Christoph Perger

International Institute for Applied Systems Analysis

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Mathias Karner

International Institute for Applied Systems Analysis

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M. Lesiv

International Institute for Applied Systems Analysis

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D. Schepaschenko

International Institute for Applied Systems Analysis

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D. Domian

International Institute for Applied Systems Analysis

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O. Danylo

International Institute for Applied Systems Analysis

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