Automatic Selection of Stochastic Watershed Hierarchies

International audienceThe segmentation, seen as the association of a partition with an image, is a difficult task. It can be decomposed in two steps: at first, a family of contours associated with a series of nested partitions (or hierarchy) is created and organized, then pertinent contours are extracted. A coarser partition is obtained by merging adjacent regions of a finer partition. The strength of a contour is then measured by the level of the hierarchy for which its two adjacent regions merge. We present an automatic segmentation strategy using a wide range of stochastic watershed hierarchies. For a given set of homogeneous images, our approach selects automatically the best hierarchy and cut level to perform image simplification given an evaluation score. Experimental results illustrate the advantages of our approach on several real-life images datasets

Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers

Scene parsing, or semantic segmentation, consists in labeling each pixel in an image with the category of the object it belongs to. It is a challenging task that involves the simultaneous detection, segmentation and recognition of all the objects in the image. The scene parsing method proposed here starts by computing a tree of segments from a graph of pixel dissimilarities. Simultaneously, a set of dense feature vectors is computed which encodes regions of multiple sizes centered on each pixel. The feature extractor is a multiscale convolutional network trained from raw pixels. The feature vectors associated with the segments covered by each node in the tree are aggregated and fed to a classifier which produces an estimate of the distribution of object categories contained in the segment. A subset of tree nodes that cover the image are then selected so as to maximize the average "purity" of the class distributions, hence maximizing the overall likelihood that each segment will contain a single object. The convolutional network feature extractor is trained end-to-end from raw pixels, alleviating the need for engineered features. After training, the system is parameter free. The system yields record accuracies on the Stanford Background Dataset (8 classes), the Sift Flow Dataset (33 classes) and the Barcelona Dataset (170 classes) while being an order of magnitude faster than competing approaches, producing a 320 \times 240 image labeling in less than 1 second.Comment: 9 pages, 4 figures - Published in 29th International Conference on Machine Learning (ICML 2012), Jun 2012, Edinburgh, United Kingdo

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Adapting Urban Water Systems to Manage Scarcity in the 21st Century: The Case of Los Angeles.

Acute water shortages for large metropolitan regions are likely to become more frequent as climate changes impact historic precipitation levels and urban population grows. California and Los Angeles County have just experienced a severe four year drought followed by a year of high precipitation, and likely drought conditions again in Southern California. We show how the embedded preferences for distant sources, and their local manifestations, have created and/or exacerbated fluctuations in local water availability and suboptimal management. As a socio technical system, water management in the Los Angeles metropolitan region has created a kind of scarcity lock-in in years of low rainfall. We come to this through a decade of coupled research examining landscapes and water use, the development of the complex institutional water management infrastructure, hydrology and a systems network model. Such integrated research is a model for other regions to unpack and understand the actual water resources of a metropolitan region, how it is managed and potential ability to become more water self reliant if the institutions collaborate and manage the resource both parsimoniously, but also in an integrated and conjunctive manner. The Los Angeles County metropolitan region, we find, could transition to a nearly water self sufficient system

Analysis of incentive contracts for sustainable ecosystem management in the buffer zone of Podocarpus National Park, Ecuador

Payments for environmental services (PES) schemes have been proposed to create positive incentives to landholders to safeguard certain ecosystems. In addition, PES are often considered as a tool to improve rural household income. The aim of this PhD research is to analyse the (potential) impacts and trade-offs of PES contracts for the maintenance and improvement of ecosystem services and the delivery of rural income to households in the buffer zone of Podocarpus National Park in Ecuador. Overall we see a trade-off between conserving and improving ecosystem service provision, and increasing household income in the PES schemes analysed. Within the research area, rural poverty seems best to be addressed by the implementation of projects that aim at increasing productivity. These could be linked with environmentally friendly practices to ensure a certain level of ecosystem service provision. PES could only have a positive impact if payments would be increased substantially for poorer households. However, PES can provide an additional and stable income source of income for forest holders. For non-forest holders PES for productive actions could also provide an additional source of income, and could strengthen the assurance that the new land use practices continue to be implemented for the duration of the contract. To improve their impact the broader institutional, economic and social environment should be considered. PES is only one tool available to governments and other actors interested in improving and sustaining ecosystem service provision. PES is influenced by a wide range of factors such as market prices for agricultural products and the national and local political situation. Within the research area, PES could be implemented as part of a policy mix and of different programmes to achieve the best outcome both in terms of ecosystem provision and rural development

CAPRi technical workshop on Watershed Management Institutions: a summary paper

The System-wide Program for Collective Action and Property Rights (CAPRi) sponsored a workshop on Watershed Management Institutions, March 13-16, 1999 in Managua, Nicaragua. The workshop focused on methodologies for undertaking research on watersheds, particularly those issues and tools that enable a more thorough understanding of the complex interactions between the biophysical factors and socioeconomic institutions of watersheds. Both social and biophysical scientists from CGIAR and other research institutions were brought together to present research and participate in focused discussions on methodologies for addressing collective action and property rights, scale, participation, and impact assessment. The forum also provided an opportunity for participants to visit and learn from a watershed project being implemented by the International Center for Tropical Agriculture (CIAT), and to discuss one another's ongoing watershed research project experience and explore opportunities for collaboration.International Food Policy Research Institute (IFPRI), International Center for Tropical Agriculture (CIAT), Impact assessment,

Multidimensional modeling and analysis of large and complex watercourse data: an OLAP-based solution

International audienceThis paper presents the application of Data Warehouse (DW) and On-Line Analytical Processing (OLAP) technologies to the field of water quality assessment. The European Water Framework Directive (DCE, 2000) underlined the necessity of having operational tools to help in the interpretation of the complex and abundant information regarding running waters and their functioning. Several studies have exemplified the interest in DWs for integrating large volumes of data and in OLAP tools for data exploration and analysis. Based on free software tools, we propose an extensible relational OLAP system for the analysis of physicochemical and hydrobiological watercourse data. This system includes: (i) two data cubes; (ii) an Extract, Transform and Load (ETL) tool for data integration; and (iii) tools for OLAP exploration. Many examples of OLAP analysis (thematic, temporal, spatiotemporal, and multiscale) are provided. We have extended an existing framework with complex aggregate functions that are used to define complex analysis indicators. Additional analysis dimensions are also introduced to allow their calculation and also for purposes of rendering information. Finally, we propose two strategies to address the problem of summarizing heterogeneous measurement units by: (i) transforming source data at the ETL tier, and (ii) introducing an additional analysis dimension at the OLAP server tier