Volunteered remote sensing data generation with air passengers as sensors

ABSTRACT Remote sensing satellites are playing very important roles in diverse earth observation fields. However, long revisit period, high cost and dense cloud cover have been the main limitations of satellite remote sensing for a long time. This paper introduces the novel volunteered passenger aircraft remote sensing (VPARS) concept, which can partly overcome these problems. By obtaining aerial imaging data from passengers using a portable smartphone on a passenger aircraft, it has various advantages including low cost, high revisit, dense coverage, and partial anti-cloud, which can well complement conventional remote sensing data. This paper examines the concept of VPARS and give general data processing framework of VPARS. Several cases were given to validate this processing approach. Two preliminary applications on land cover classification and economic activity monitoring validate the applicability of the VPARS data. Furthermore, we examine the issues about data maintenance, potential applications, limitations and challenges. We conclude the VPARS can benefit both scientific and industrial communities who rely on remote sensing data.

[1]  Paolo Gamba,et al.  Remote Sensing and Earthquake Damage Assessment: Experiences, Limits, and Perspectives , 2012, Proceedings of the IEEE.

[2]  Farid Karimipour,et al.  Citizens as Expert Sensors: One Step Up on the VGI Ladder , 2014, LBS.

[3]  Lei Wang,et al.  Fuzzy AutoEncode Based Cloud Detection for Remote Sensing Imagery , 2017, Remote. Sens..

[4]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[5]  Qingquan Li,et al.  A Bilevel Scale-Sets Model for Hierarchical Representation of Large Remote Sensing Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[6]  M. Piras,et al.  Smartphone-Based Photogrammetry for the 3D Modeling of a Geomorphological Structure , 2019, Applied Sciences.

[7]  Zhihao Qin,et al.  An Efficient Approach to Remove Thick Cloud in VNIR Bands of Multi-Temporal Remote Sensing Images , 2019, Remote. Sens..

[8]  WhiteheadKen,et al.  Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: a review of progress and challenges1 , 2014 .

[9]  Piero Boccardo,et al.  Remote Sensing Role in Emergency Mapping for Disaster Response , 2015 .

[10]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[11]  Michael F. Goodchild,et al.  Please Scroll down for Article International Journal of Digital Earth Crowdsourcing Geographic Information for Disaster Response: a Research Frontier Crowdsourcing Geographic Information for Disaster Response: a Research Frontier , 2022 .

[12]  Paul J. Crutzen,et al.  CARIBIC—Civil Aircraft for Global Measurement of Trace Gases and Aerosols in the Tropopause Region , 1999 .

[13]  Leif Kobbelt,et al.  City Reconstruction and Visualization from Public Data Sources , 2016, UDMV.

[14]  Paul J. Crutzen,et al.  Civil Aircraft for the regular investigation of the atmosphere based on an instrumented container: The new CARIBIC system , 2007 .

[15]  J. Kučera,et al.  Global trends in satellite-based emergency mapping , 2016, Science.

[16]  Christian Heipke,et al.  Crowdsourcing geospatial data , 2010 .

[17]  Charles K. Toth,et al.  Remote sensing platforms and sensors: A survey , 2016 .

[18]  Michael F. Goodchild,et al.  Assuring the quality of volunteered geographic information , 2012 .

[19]  Richard Szeliski,et al.  Modeling the World from Internet Photo Collections , 2008, International Journal of Computer Vision.

[20]  M. Silver,et al.  APPLYING SATELLITE DATA SOURCES IN THE DOCUMENTATION AND LANDSCAPE MODELLING FOR GRAECO-ROMAN/BYZANTINE FORTIFIED SITES IN THE TŪR ABDIN AREA, EASTERN TURKEY , 2017 .

[21]  Jie Shan,et al.  Technical evaluation for mashing up crowdsourcing images , 2015, 2015 23rd International Conference on Geoinformatics.

[22]  Qihao Weng,et al.  Spatiotemporally enhancing time-series DMSP/OLS nighttime light imagery for assessing large-scale urban dynamics , 2017 .

[23]  Andrew W. Fitzgibbon,et al.  Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.

[24]  M. Haklay Citizen Science and Volunteered Geographic Information: Overview and Typology of Participation , 2013 .

[25]  Jian Sun,et al.  Single image haze removal using dark channel prior , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Yin Pan,et al.  Cloud Detection in Remote Sensing Images Based on Multiscale Features-Convolutional Neural Network , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Richard Szeliski,et al.  Building Rome in a day , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[28]  Hiroji Tsu,et al.  The ASTER Global DEM , 2010 .

[29]  Liping Di,et al.  The state of the art of spaceborne remote sensing in flood management , 2016, Natural Hazards.

[30]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[31]  Brenner Silva,et al.  Mapping Two Competing Grassland Species from a Low-Altitude Helium Balloon , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  K. Mcdougall,et al.  The use of LiDAR and volunteered geographic information to map flood extents and inundation , 2012 .

[33]  Ying Wang,et al.  Urban Observation: Integration of Remote Sensing and Social Media Data , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  M. Goodchild,et al.  Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice , 2012 .

[35]  Akira Hirano,et al.  Mapping from ASTER stereo image data: DEM validation and accuracy assessment , 2003 .

[36]  Mark S. Strauss Planet Earth to get a daily selfie. , 2017, Science.

[37]  Qingquan Li,et al.  A random forest classifier based on pixel comparison features for urban LiDAR data , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[38]  Perry C. Oddo,et al.  The Value of Near Real-Time Earth Observations for Improved Flood Disaster Response , 2019, Front. Environ. Sci..

[39]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .

[40]  Carolynne Hultquist,et al.  Integration of Crowdsourced Images, USGS Networks, Remote Sensing, and a Model to Assess Flood Depth during Hurricane Florence , 2020, Remote. Sens..

[41]  Charalabos Ioannidis,et al.  Oblique aerial images: a review focusing on georeferencing procedures , 2018 .

[42]  Hansi Senaratne,et al.  A review of volunteered geographic information quality assessment methods , 2017, Int. J. Geogr. Inf. Sci..

[43]  M. Sojka,et al.  Ground volume assessment using ’Structure from Motion’ photogrammetry with a smartphone and a compact camera , 2017 .