scholarly journals Synergy between Gmes and Regional Innovation Strategies: Very High Resolution Images for Local Planning and Monitoring

2012 ◽  
Vol 45 (1) ◽  
pp. 305-315 ◽  
Author(s):  
Giovanni Sylos Labini ◽  
Daniela Drimaco ◽  
Paolo Manunta ◽  
Adriana Agrimi ◽  
Guido Pasquariello
Keyword(s):  
High Resolution ◽  
Local Planning ◽  
Regional Innovation ◽  
High Resolution Images ◽  
Innovation Strategies ◽  
Very High

scholarly journals Cloud Computation Using High-Resolution Images for Improving the SDG Indicator on Open Spaces

2020 ◽  
Vol 12 (7) ◽  
pp. 1144
Author(s):  
Rosa Aguilar ◽  
Monika Kuffer
Keyword(s):  
High Resolution ◽  
Open Space ◽  
Local Planning ◽  
Open Spaces ◽  
High Resolution Imagery ◽  
Coarse Spatial Resolution ◽  
Multi Temporal ◽  
High Resolution Images ◽  
Cloud Computation

Open spaces are essential for promoting quality of life in cities. However, accelerated urban growth, in particular in cities of the global South, is reducing the often already limited amount of open spaces with access to citizens. The importance of open spaces is promoted by SDG indicator 11.7.1; however, data on this indicator are not readily available, neither globally nor at the metropolitan scale in support of local planning, health and environmental policies. Existing global datasets on built-up areas omit many open spaces due to the coarse spatial resolution of input imagery. Our study presents a novel cloud computation-based method to map open spaces by accessing the multi-temporal high-resolution imagery repository of Planet. We illustrate the benefits of our proposed method for mapping the dynamics and spatial patterns of open spaces for the city of Kampala, Uganda, achieving a classification accuracy of up to 88% for classes used by the Global Human Settlement Layer (GHSL). Results show that open spaces in the Kampala metropolitan area are continuously decreasing, resulting in a loss of open space per capita of approximately 125 m2 within eight years.

scholarly journals Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 320
Author(s):  
Emilio Guirado ◽  
Javier Blanco-Sacristán ◽  
Emilio Rodríguez-Caballero ◽  
Siham Tabik ◽  
Domingo Alcaraz-Segura ◽  
...  
Keyword(s):  
High Resolution ◽  
Optical Sensors ◽  
Biotic Interactions ◽  
Relevant Information ◽  
Vegetation Monitoring ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Segmentation Methods ◽  
High Resolution Images ◽  
Very High

Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.

Hands Stencils in El Castillo Cave (Puente Viesgo, Cantabria, Spain). An Interdisciplinary Study

2021 ◽  
pp. 1-21
Author(s):  
Sergio Ripoll ◽  
Vicente Bayarri ◽  
Francisco J. Muñoz ◽  
Ricardo Ortega ◽  
Elena Castillo ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Data ◽  
Digital Processing ◽  
The Other ◽  
Major Step ◽  
The People ◽  
High Resolution Images ◽  
Real Picture ◽  
Interdisciplinary Study ◽  
Very High

Our Palaeolithic ancestors did not make good representations of themselves on the rocky surfaces of caves and barring certain exceptions – such as the case of La Marche (found on small slabs of stone or plaquettes) or the Cueva de Ambrosio – the few known examples can only be referred to as anthropomorphs. As such, only hand stencils give us a real picture of the people who came before us. Hand stencils and imprints provide us with a large amount of information that allows us to approach not only their physical appearance but also to infer less tangible details, such as the preferential use of one hand over the other (i.e., handedness). Both new and/or mature technologies as well as digital processing of images, computers with the ability to process very high resolution images, and a more extensive knowledge of the Palaeolithic figures all help us to analyse thoroughly the hands in El Castillo cave. The interdisciplinary study presented here contributes many novel developments based on real data, representing a major step forward in knowledge about our predecessors.

Spectral information extraction from very high resolution images through multiresolution fusion

2004 ◽  
Author(s):  
Luciano Alparone ◽  
Bruno Aiazzi ◽  
Stefano Baronti ◽  
Andrea Garzelli ◽  
Filippo Nencini ◽  
...  
Keyword(s):  
High Resolution ◽  
Information Extraction ◽  
Spectral Information ◽  
High Resolution Images ◽  
Very High
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