@AudiovisualMaterial{AssisFerVinNovZip:2018:DiCrGe,
abstract = "The effective monitoring of land-use and land-cover changes
(LULCC) is a basic requirement for understanding
socio-enviromental processes of local to global scales. Remote
sensing data and methods have long been established as the most
effective approach for monitoring LULCC. The potential for further
increasing the effectiveness of this approach is proportional to
the astonishingly large amount of satellite imagery provided, many
times free-of-cost, by space agencies worldwide. However,
scientists still lack of ways of organizing, structuring and
analyzing this colossal amount of remote sensing data in a way
that leverages administrative and scientific LULCC monitoring.
Hence, an efficient image data storage, query and processing
architecture that manages different satellite specifications and
climatic conditions is required for generating and sharing updated
and area-extensive LULCC information. Furthermore, because
reliable LULCC monitoring with remote sensing data requires
extensive training and validation analysis performed by humans,
the potential of big Earth Observation (EO) data for LULCC
monitoring is still limited by the amount and time- availability
of the analysts involved in the project. In this paper, we discuss
the potential of Citizen Science for improving the feasibility and
effectiveness of LULCC monitoring supported by big EO data
architectures. We put forward general ideas on how to promote and
stimulate an active involvement of citizens in EO data analytics
for LULCC monitoring. For that, we briefly present and critically
evaluate how existing approaches that allow citizens to contribute
with up-to-date and detailed LULCC information mitigate the issue
of exhaustive sampling required in LULCC monitoring with automatic
remote sensing image classification.",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Heidelberg University} and
{Heidelberg University}",
author = "Assis, Luiz Fernando Ferreira Gomes de and Ferreira, Karine Reis
and Vinhas, L{\'u}bia and Novack, Tessio and Zipf, Alexander",
city = "Lund, Sweden",
conferencename = "VGI-ALIVE: Analysis Integration, Vision, Engagement",
date = "12 june",
keywords = "Citizen Science, Land-Use/Land-Cover, Remote Sensing, Big Earth
Observation Data.",
language = "en",
publisher = "Instituto Nacional de Pesquisas Espaciais",
publisheraddress = "S{\~a}o Jos{\'e} dos Campos",
targetfile = "assis_discussion_apresentacao.pdf",
title = "A discussion of crowdsourced geographic information initiatives
and big Earth observation data architectures for land-use and
land-cover monitoring",
year = "2018",
urlaccessdate = "19 abr. 2024"
}