scholarly journals Information-optimal Abstaining for Reliable Classification of Building Functions

2021 ◽  
Vol 2 ◽  
pp. 1-10
Author(s):  
Gabriel Dax ◽  
Martin Werner
Keyword(s):  
Regional Scale ◽  
Sensor Technology ◽  
Observation Data ◽  
Classification Schemes ◽  
The Past ◽  
Social Media Text ◽  
Novel Method ◽  
Earth Observation Data ◽  
Reliable Classification

Abstract. In the past decade, major breakthroughs in sensor technology and algorithms have enabled the functional analysis of urban regions based on Earth observation data. It has, for example, become possible to assign functions to areas in cities on a regional scale. With this paper, we develop a novel method for extracting building functions from social media text alone. Therefore, a technique of abstaining is applied in order to overcome the fact that most tweets will not contain information related to a building function albeit they have been sent from a specific building as well as the problem that classification schemes for building functions are overlapping.

scholarly journals A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 110
Author(s):  
Filippo Sarvia ◽  
Elena Xausa ◽  
Samuele De Petris ◽  
Gianluca Cantamessa ◽  
Enrico Borgogno-Mondino
Keyword(s):  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Observation Data ◽  
Key Factor ◽  
Ndvi Time Series ◽  
Earth Observation Data ◽  
Four Levels ◽  
Nw Italy ◽  
Sentinel 2

Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology to control CAP applications based on Earth Observation data. Accordingly, this work was aimed at designing and implementing a prototype of service based on Copernicus Sentinel-2 (S2) data for the classification of soybean, corn, wheat, rice, and meadow crops. The approach relies on the classification of S2 NDVI time-series (TS) by “user-friendly” supervised classification algorithms: Minimum Distance (MD) and Random Forest (RF). The study area was located in the Vercelli province (NW Italy), which represents a strategic agricultural area in the Piemonte region. Crop classes separability proved to be a key factor during the classification process. Confusion matrices were generated with respect to ground checks (GCs); they showed a high Overall Accuracy (>80%) for both MD and RF approaches. With respect to MD and RF, a new raster layer was generated (hereinafter called Controls Map layer), mapping four levels of classification occurrences, useful for administrative procedures required by PA. The Control Map layer highlighted that only the eight percent of CAP 2019 applications appeared to be critical in terms of consistency between farmers’ declarations and classification results. Only for these ones, a GC was warmly suggested, while the 12% must be desirable and the 80% was not required. This information alone suggested that the proposed methodology is able to optimize GCs, making possible to focus ground checks on a limited number of fields, thus determining an economic saving for PA and/or a more effective strategy of controls.

scholarly journals Determination of Phycocyanin from Space—A Bibliometric Analysis

2020 ◽  
Vol 12 (3) ◽  
pp. 567
Author(s):  
Igor Ogashawara
Keyword(s):  
Remote Sensing ◽  
Bibliometric Analysis ◽  
Water Quality Monitoring ◽  
Development Trend ◽  
Future Research ◽  
Observation Data ◽  
The Past ◽  
The Status ◽  
Earth Observation Data ◽  
Remote Sensing Techniques

Over the past few decades, there has been an increase in the number of studies about the estimation of phycocyanin derived from remote sensing techniques. Since phycocyanin is a unique pigment of inland water cyanobacteria, the quantification of its concentration from earth observation data is important for water quality monitoring - once some species can produce toxins. Because of the growth of this field in the past decade, several reviews and studies comparing algorithms have been published. Thus, instead of focusing on algorithms comparison or description, the goal of the present study is to systematically analyze and visualize the evolution of publications. Using the Web of Science database this study analyzed the existing publications on remote sensing of phycocyanin decade-by-decade for the period 1991–2020. The bibliometric analysis showed how research topics evolved from measuring pigments to the quantification of optical properties and from laboratory experiments to measuring entire temperate and tropical aquatic systems. This study provides the status quo and development trend of the field and points out what could be the direction for future research.

scholarly journals TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION

2016 ◽  
Vol XLI-B3 ◽  
pp. 283-287
Author(s):  
N. Li ◽  
C. Liu ◽  
N. Pfeifer ◽  
J. F. Yin ◽  
Z.Y. Liao ◽  
...  
Keyword(s):  
Tensor Decomposition ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Observation Data ◽  
Multiple Features ◽  
Key Factor ◽  
Earth Observation Data ◽  
Airborne Lidar Data ◽  
Nearest Neighborhood

Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the “raw” data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could keep the initial spatial structure and insure the consideration of the neighborhood. Based on a small number of component features a k nearest neighborhood classification is applied.

On the barriers limiting the adoption of the Earth Observation Copernicus data and services and their integration with non-conventional (e.g. citizen) observations: the EU CoRdiNet project contribution.

2020 ◽  
Author(s):  
Teodosio Lacava ◽  
Lucio Bernardini Papalia ◽  
Iole Federica Paradiso ◽  
Monica Proto ◽  
Nicola Pergola
Keyword(s):  
Regional Scale ◽  
Earth Observation ◽  
Small Enterprises ◽  
Observation Data ◽  
The European Union ◽  
Horizon 2020 ◽  
New Business ◽  
Earth Observation Data ◽  
Regional Authorities

<p>The Copernicus User Uptake Initiative is part of the European Union’s strategy for increasing the level of awareness of the Copernicus Program at European and worldwide level, fostering the adoption of Copernicus-based data/solution in the everyday life of each kind of potential stakeholder, from Local Regional Authorities (LRA) to Big and/or Small Enterprises to normal citizens. The CoRdiNet (Copernicus Relays for digitalization spanning a Network) projects was funded in the frame of Horizon 2020 Space Hubs call (grant agreement n. 821911), to implement and reinforce the user uptake actions among the network of the so called Copernicus Relays. The latter, as part of the Space strategy for Europe of the European Commission, act as Copernicus Ambassadors, providing their contribution for a better dissemination and promotion of Copernicus-based solution at local/regional scale. Among the goals of the Cordinet project there are: i) Supporting, promoting and stimulating digitalization and new business solutions based on Earth observation data from the Copernicus project; ii) bundling the local expertise in the civil use of Earth observation close to the needs and offers of citizens, administration and businesses.</p><p>Earth Observation data from space, in fact, can provide products and services to citizens and can be profitably integrated with non-conventional data, e.g the ones coming from citizen observatories and sciences. However, presently Copernicus data and information are still under-exploited and further efforts are needed to engage stakeholders (including normal citizens), investigating the causes that have prevented from a more systematic and diffuse use of Copernicus/EO data so far. In fact, an increased awareness about the Copernicus program, its data, products and services, will allow for a better integration of non-conventional (e.g. citizen-based) observations, enabling new services and solutions, more close to the citizen needs and requirements for a better quality of life.</p><p>With this aim, one of the tasks of the project was specifically devoted to the identification and engagement of the stakeholders within the CoRdiNet partner geographic regions, including also the external ones involved by a specific call for expression of interest, and it was carried out by TeRN in collaboration with CNR-IMAA. In particular, after their engagement, stakeholders were asked to answer to a questionnaire aimed at analyzing their needs and capabilities and evaluating which barriers have prevented for a more systematic use of Copernicus solutions so far in their own activities. Results achieved analyzing collected feedback will be presented and discussed in this work, providing also a few preliminary recommendations about how to cope with the identified gaps.</p>

Australia's National Classification System for Publications, Films and Computer Games: Its Operation and Potential Susceptibility to Political Influence in Classification Decisions

2009 ◽  
Vol 37 (1) ◽  
pp. 133-157
Author(s):  
Michael Ramaraj Dunstan
Keyword(s):  
Computer Games ◽  
Classification System ◽  
Decision Process ◽  
Current System ◽  
Regulatory System ◽  
Political Influence ◽  
Classification Schemes ◽  
The Past ◽  
Cooperative Scheme

The purpose of this article is to examine Australia's regulatory system for the classification of publications, films and computer games, the National Classification System (‘NCS’), and to question whether its classification decision process is susceptible to political influence. Formed in 1995 as a cooperative scheme between the Commonwealth, States and Territories, the NCS was created to overcome problems associated with former classification schemes that operated on a non-national basis in each Australian jurisdiction. It is argued that, although the current system is superior to the ones of the past, it still allows, or at least perceivably allows, political influence in censorship decision-making, as was historically the case. This is because documents used by the Classification Board and Classification Review Board (‘the Boards’) to make classification decisions are ambiguous and often inconsistent, and, even with redrafting, would remain so without the benefit of judicial precedent. The ambiguity created by the classification documents legitimates the possible exercise of political influence through a variety of means.

scholarly journals TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION

2016 ◽  
Vol XLI-B3 ◽  
pp. 283-287
Author(s):  
N. Li ◽  
C. Liu ◽  
N. Pfeifer ◽  
J. F. Yin ◽  
Z.Y. Liao ◽  
...  
Keyword(s):  
Tensor Decomposition ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Observation Data ◽  
Multiple Features ◽  
Key Factor ◽  
Earth Observation Data ◽  
Airborne Lidar Data ◽  
Nearest Neighborhood

Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the “raw” data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could keep the initial spatial structure and insure the consideration of the neighborhood. Based on a small number of component features a k nearest neighborhood classification is applied.

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