Rural women's access to credit

To enhance the role of rural women in development, NGOs have assisted in the establishment of credit programs.

An idealized rural coastal zone management integrating land and water use

Various countries have formulated special integrated coastal zone management (ICZM) strategies which seek to both manage development and conserve natural resources and integrate and coordinate the relevant people sectors and their functions and roles within the bounds of this rich realm. Concerns that may be addressed by ICZM include: 1) Natural resources degradation; 2) Pollution; 3) Land use conflicts; and, 4) Destruction of life and property by natural hazards. Some prevalent sources of environmental impacts (livelihoods) are listed, together with some recommendations to the concerns which they may raise in relation to coastal zone management: agriculture; aquaculture; fisheries; forestry; human settlements; tourism; and, transport industry.

The value of cooperatives in rural electrification

The electricity sectors of many developing countries underwent substantial reforms during the 1980s and 1990s, driven by global agendas of privatization and liberalization. However, rural electrification offered little by way of market incentives for profit-seeking private companies and was often neglected. As a consequence, delivery models for rural electrification need to change. This paper will review the experiences of various rural electrification delivery models that have been established in developing countries, including concessionary models, dealership approaches and the strengthening of small and medium-sized energy businesses. It will use examples from the USA, Bangladesh and Nepal, together with a detailed case study of a Nepali rural electric cooperative, to explore the role that local cooperatives can play in extending electricity access. It is shown that although there is no magic bullet solution to deliver rural electrification, if offered appropriate financial and institutional support, socially orientated cooperative businesses can be a willing, efficient and effective means of extending and managing rural electricity services. It is expected that this paper will be of particular value to policy-makers, donors, project planners and implementers currently working in the field of rural electrification. © 2010 Elsevier Ltd.

Low-carbon off-grid electrification for rural areas in the United Kingdom: Lessons from the developing world

Low-carbon off-grid electrification for rural areas is becoming increasingly popular in the United Kingdom. However, many developing countries have been electrifying their rural areas in this way for decades. Case study fieldwork in Nepal and findings from United Kingdom based research will be used to examine how developed nations can learn from the experience of developing countries with regard to the institutional environment and delivery approach adopted in renewable energy off-grid rural electrification. A clearer institutional framework and more direct external assistance during project development are advised. External coordinators should also engage the community in a mobilization process a priori to help alleviate internal conflicts of interest that could later impede a project. © 2011 Elsevier Ltd.

Progressive 3D reconstruction of infrastructure with videogrammetry

A number of methods are commonly used today to collect infrastructure's spatial data (time-of-flight, visual triangulation, etc.). However, current practice lacks a solution that is accurate, automatic, and cost-efficient at the same time. This paper presents a videogrammetric framework for acquiring spatial data of infrastructure which holds the promise to address this limitation. It uses a calibrated set of low-cost high resolution video cameras that is progressively traversed around the scene and aims to produce a dense 3D point cloud which is updated in each frame. It allows for progressive reconstruction as opposed to point-and-shoot followed by point cloud stitching. The feasibility of the framework is studied in this paper. Required steps through this process are presented and the unique challenges of each step are identified. Results specific to each step are also presented.

Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features

The commercial far-range (>10 m) spatial data collection methods for acquiring infrastructure’s geometric data are not completely automated because of the necessary manual pre- and/or post-processing work. The required amount of human intervention and, in some cases, the high equipment costs associated with these methods impede their adoption by the majority of infrastructure mapping activities. This paper presents an automated stereo vision-based method, as an alternative and inexpensive solution, to producing a sparse Euclidean 3D point cloud of an infrastructure scene utilizing two video streams captured by a set of two calibrated cameras. In this process SURF features are automatically detected and matched between each pair of stereo video frames. 3D coordinates of the matched feature points are then calculated via triangulation. The detected SURF features in two successive video frames are automatically matched and the RANSAC algorithm is used to discard mismatches. The quaternion motion estimation method is then used along with bundle adjustment optimization to register successive point clouds. The method was tested on a database of infrastructure stereo video streams. The validity and statistical significance of the results were evaluated by comparing the spatial distance of randomly selected feature points with their corresponding tape measurements.

Visual pattern recognition models for remote sensing of civil infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.

Comparison of Civil Infrastructure Optical-based Spatial Data Acquisition Techniques

Infrastructure spatial data, such as the orientation and the location of in place structures and these structures' boundaries and areas, play a very important role for many civil infrastructure development and rehabilitation applications, such as defect detection, site planning, on-site safety assistance and others. In order to acquire these data, a number of modern optical-based spatial data acquisition techniques can be used. These techniques are based on stereo vision, optics, time of flight, etc., and have distinct characteristics, benefits and limitations. The main purpose of this paper is to compare these infrastructure optical-based spatial data acquisition techniques based on civil infrastructure application requirements. In order to achieve this goal, the benefits and limitations of these techniques were identified. Subsequently, these techniques were compared according to applications' requirements, such as spatial accuracy, the automation of acquisition, the portability of devices and others. With the help of this comparison, unique characteristics of these techniques were identified so that practitioners will be able to select an appropriate technique for their own applications.

Comparison of Civil Infrastructure Optical-based Spatial Data Acquisition Techniques

Infrastructure spatial data, such as the orientation and the location of in place structures and these structures' boundaries and areas, play a very important role for many civil infrastructure development and rehabilitation applications, such as defect detection, site planning, on-site safety assistance and others. In order to acquire these data, a number of modern optical-based spatial data acquisition techniques can be used. These techniques are based on stereo vision, optics, time of flight, etc., and have distinct characteristics, benefits and limitations. The main purpose of this paper is to compare these infrastructure optical-based spatial data acquisition techniques based on civil infrastructure application requirements. In order to achieve this goal, the benefits and limitations of these techniques were identified. Subsequently, these techniques were compared according to applications' requirements, such as spatial accuracy, the automation of acquisition, the portability of devices and others. With the help of this comparison, unique characteristics of these techniques were identified so that practitioners will be able to select an appropriate technique for their own applications.

Visual Pattern Recognition Models for Remote Sensing of Civil Infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.