4,400 research outputs found
Multi-objective models for the forest harvest scheduling problem in a continuous-time framework
In this study we present several multi-objective models for forest harvest scheduling in forest with single-species, even-aged stands using a continuous formulation. We seek to maximize economic profitability and even-flow of timber harvest volume, both for the first rotation and for the regulated forest. For that, we design new metrics that allow working with continuous decision variables, namely, the harvest time of each stand. Unlike traditional combinatorial formulations, this avoids dividing the planning horizon into periods and simulating alternative management prescriptions before the optimization process. We propose to combine a scalarization technique (weighting method) with a gradient-type algorithm (L-BFGS-B) to obtain the Pareto frontier of the problem, which graphically shows the relationships (trade-offs) between objectives, and helps the decision makers to choose a suitable weighting for each objective. We compare this approach with the widely used in forestry multi-objective evolutionary algorithm NSGA-II. We analyze the model in a Eucalyptus globulus Labill. forest of Galicia (NW Spain). The continuous formulation proves robust in forests with different structures and provides better results than the traditional combinatorial approach. For problem solving, our proposal shows a clear advantage over the evolutionary algorithm in terms of computational time (efficiency), being of the order of 65 times faster for both continuous and discrete formulationsS
Modelos multiobjetivo para la planificación forestal en Galicia: un enfoque continuo
En esta tesis se presentan varios modelos multiobjetivo para la planificación
de cortas en montes constituidos por rodales regulares monoespecíficos (los más habituales en Galicia), utilizando
una formulación temporal continua que busca maximizar la rentabilidad económica y la renta constante en
volumen en el primer turno y/o en el monte regulado. Para ello, se han diseñado nuevas métricas para medir esa
constancia de rentas que permiten trabajar con variables de decisión continuas. Para la resolución de los
problemas, se propone una estrategia que combina una técnica de escalarización (método de pesos) con un
algoritmo de tipo gradiente (L-BFGS-B) que permite obtener el frente Pareto. Esto posibilita que el tomador de
decisiones especifique sus preferencias con posterioridad al proceso de optimización, ya que dispone de toda la
información sobre los costes de oportunidad que asume cuando elige una solución satisfactoria entre el conjunto
de soluciones no dominadas del frente. Con el fin de analizar el rendimiento de la estrategia planteada, se
comparan los resultados que proporciona con los obtenidos mediante el algoritmo evolutivo para optimización
multiobjetivo NSGA-II. Para evaluar los modelos propuestos se usan varios montes de Eucalyptus globulus Labill.
La formulación continua ha demostrado ser robusta en montes con distinto número de rodales y diferente
estructura de edades, y ha proporcionado resultados mejores que los obtenidos con el enfoque combinatorio
tradicional. Con respecto a la resolución de los problemas, la estrategia propuesta ha resultado claramente más
eficiente que el algoritmo evolutivo, y tanto más eficaz cuanto mayor ha sido la dimensión del problema
Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach
This paper introduces Quicksilver, a fast deformable image registration
method. Quicksilver registration for image-pairs works by patch-wise prediction
of a deformation model based directly on image appearance. A deep
encoder-decoder network is used as the prediction model. While the prediction
strategy is general, we focus on predictions for the Large Deformation
Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the
momentum-parameterization of LDDMM, which facilitates a patch-wise prediction
strategy while maintaining the theoretical properties of LDDMM, such as
guaranteed diffeomorphic mappings for sufficiently strong regularization. We
also provide a probabilistic version of our prediction network which can be
sampled during the testing time to calculate uncertainties in the predicted
deformations. Finally, we introduce a new correction network which greatly
increases the prediction accuracy of an already existing prediction network. We
show experimental results for uni-modal atlas-to-image as well as uni- / multi-
modal image-to-image registrations. These experiments demonstrate that our
method accurately predicts registrations obtained by numerical optimization, is
very fast, achieves state-of-the-art registration results on four standard
validation datasets, and can jointly learn an image similarity measure.
Quicksilver is freely available as an open-source software.Comment: Add new discussion
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification
Designing discriminative powerful texture features robust to realistic
imaging conditions is a challenging computer vision problem with many
applications, including material recognition and analysis of satellite or
aerial imagery. In the past, most texture description approaches were based on
dense orderless statistical distribution of local features. However, most
recent approaches to texture recognition and remote sensing scene
classification are based on Convolutional Neural Networks (CNNs). The d facto
practice when learning these CNN models is to use RGB patches as input with
training performed on large amounts of labeled data (ImageNet). In this paper,
we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained
using mapped coded images with explicit texture information provide
complementary information to the standard RGB deep models. Additionally, two
deep architectures, namely early and late fusion, are investigated to combine
the texture and color information. To the best of our knowledge, we are the
first to investigate Binary Patterns encoded CNNs and different deep network
fusion architectures for texture recognition and remote sensing scene
classification. We perform comprehensive experiments on four texture
recognition datasets and four remote sensing scene classification benchmarks:
UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with
7 categories and the recently introduced large scale aerial image dataset (AID)
with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary
information to standard RGB deep model of the same network architecture. Our
late fusion TEX-Net architecture always improves the overall performance
compared to the standard RGB network on both recognition problems. Our final
combination outperforms the state-of-the-art without employing fine-tuning or
ensemble of RGB network architectures.Comment: To appear in ISPRS Journal of Photogrammetry and Remote Sensin
Application of Remote Sensing to the Chesapeake Bay Region. Volume 2: Proceedings
A conference was held on the application of remote sensing to the Chesapeake Bay region. Copies of the papers, resource contributions, panel discussions, and reports of the working groups are presented
Cornell University remote sensing program
There are no author-identified significant results in this report
A deep learning approach to urban street functionality prediction based on centrality measures and stacked denoising autoencoder
ABSTRACT: In urban planning and transportation management, the centrality characteristics of urban streets are vital measures to consider. Centrality can help in understanding the structural properties of dense traffic networks that affect both human life and activity in cities. Many cities classify urban streets to provide stakeholders with a group of street guidelines for possible new rehabilitation such as sidewalks, curbs, and setbacks. Transportation research always considers street networks as a connection between different urban areas. The street functionality classification defines the role of each element of the urban street network (USN). Some potential factors such as land use mix, accessible service, design goal, and administrators’ policies can affect the movement pattern of urban travelers. In this study, nine centrality measures are used to classify the urban roads in four cities evaluating the structural importance of street segments. In our work, a Stacked Denoising Autoencoder (SDAE) predicts a street’s functionality, then logistic regression is used as a classifier. Our proposed classifier can differentiate between four different classes adopted from the U.S. Department of Transportation (USDT): principal arterial road, minor arterial road, collector road, and local road. The SDAE-based model showed that regular grid configurations with repeated patterns are more influential in forming the functionality of road networks compared to those with less regularity in their spatial structure
Examination of the Spatial Relationship between Development Metrics and Total Phosphorus in the Galveston Bay Estuary
Urban development can cause increased nutrient loads in nearby streams and rivers. Understanding how the pattern of urban development affects the level of nutrients, specifically phosphorus, within the Galveston Bay Estuary is particularly important for planners and policymakers working to maximize the water quality within the region. The problem of eutrophication that results from increased nutrients can be detrimental to the health of the ecosystem; further, the rapid population growth within the Galveston Bay Estuary is increasing development within the area. The ecosystem-based study described here examines 99 watersheds across the Galveston Bay Estuary, Texas. Multiple development metrics are evaluated for both high and low intensity development and these development patterns are related to total phosphorus as an indicator of water quality. Spatial lag models were used to determine the relationship between the high intensity and low intensity development and phosphorus levels. It was hypothesized and validated by the results that less fragmented and more connected urban development patches within the Galveston Bay Estuary relate to lower phosphorus levels. In addition, as the proportion of low intensity development increases within a watershed, phosphorus levels are also increased due to runoff from fertilizer. Phosphorus-based fertilizer runoff has increased in the region and is likely driven by the use of fertilizers on urban and rural homes. The results from this study can be implemented in planning and policy through a series of tools including development clustering, urban growth boundaries, transfer of development rights, education and outreach, and implementation of laws. Each planning tool offers a way to aggregate the low intensity development in a manner that will reduce the phosphorus levels within the study area; this, in turn, will decrease the probability of eutrophication that can result in streams with nutrient loading problems. In addition, there are a large amount of phosphorus-based fertilizers used in the region, and reducing these levels will aid in decreasing the phosphorus levels within the rivers and streams
Mapping changes in landscape-scale patterns of vegetation in coal seam gas development areas
In the 15 years to 2013, the rate of coal seam gas (CSG) development in Queensland increased dramatically. Drilling of gas wells and installation of associated infrastructure sometimes requires the removal of vegetation. Although a change in vegetation cover on a small scale does not necessarily correspond to significant landscape-‐scale change, research overseas indicates that the extent and configuration of vegetation patches in a landscape is altered in areas of concentrated oil and gas extraction. The focus of previous research has been on oil and shale gas activity, mainly in North America. There has been little work on the nature and extent of the impact of similar developments in an Australian context, or impacts due specifically to CSG activity anywhere in the world.
The aim of this study is to determine the nature of land cover change in a region of southern Queensland under intense CSG development. The extent and fragmentation of vegetation in 1999, immediately before CSG development began, is compared to the extent and fragmentation of vegetation in 2013, after 1562 coal seam gas wells had been drilled. Land cover was determined by classification of a LANDSAT 4 image taken in 1999 and a Landsat 8 image taken in 2013. ArcGIS 10.2 was used for image manipulation, and vegetation patch metrics were determined using FRAGSTATS 4.2 software. For comparison, the same metrics were also calculated in hot spot regions defined in two different ways to focus more
closely around drilling sites. Similarly, the same metrics were calculated on a classified image modified to ensure that known linear clearings were continuously defined despite the automatic classification.
The study finds that processes causing land cover change in the study area generally have a net positive effect on the landscape. Positive changes were observed despite clear evidence that CSG activity has directly led to vegetation loss on a small scale around CSG developments
within the study area. This study shows that, while CSG development has a distinguishable impact on land cover at a landscape scale in southern Queensland, other
more significant drivers of change mask the effect of CSG activity
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Models for Human Navigation and Optimal Path Planning Using Level Set Methods and Hamilton-Jacobi Equations
We present several models for different physical scenarios which are centered around human movement or optimal path planning, and use partial differential equations and concepts from control theory. The first model is a game-theoretic model for environmental crime which tracks criminals' movement using the level set method, and improves upon previous continuous models by removing overly restrictive assumptions of symmetry. Next, we design a method for determining optimal hiking paths in mountainous regions using an anisotropic level set equation. After this, we present a model for optimal human navigation with uncertainty which is rooted in dynamic programming and stochastic optimal control theory. Lastly, we consider optimal path planning for simple, self-driving cars in the Hamilton-Jacobi formulation. We improve upon previous models which simplify the car to a point mass, and present a reasonably general upwind, sweeping scheme to solve the relevant Hamilton-Jacobi equation
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