scholarly journals La gestión del vacío urbano: contextos, casos y propuestas

REVISTARQUIS ◽  
2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Ana Paula Montes
Keyword(s):  
Case Studies ◽  
Land Management ◽  
Urban Sprawl ◽  
Review Article ◽  
Costa Rican ◽  
Sustainable Land Management ◽  
Global Response ◽  
Urban Imaginary

ResumenEste artículo de revisión tiene como propósito discutir una noción ampliada del concepto de vacío urbano en el marco de una coyuntura global de crecimiento urbano disperso y una urgente gestión sostenible del territorio. Para informar sobre el estado actual de la discusión sobre el tema de los vacíos urbanos, este ensayo se ha articulado en dos secciones. La primera incluye la presentación de una serie de conceptos que justifican un contexto global de reclamo de los vacíos urbanos. La segunda sección contiene una recopilación de casos de gestión de vacíos urbanos, cuya viabilidad ha sido posible gracias a que se han sustentado en innovadoras herramientas de legislación urbana. En el contexto costarricense, en donde la escasa discusión en torno a los vacíos urbanos continua alojada en los imaginarios negativos sobre la ciudad, resulta importante visibilizar experiencias en donde esta acepción negativa ha sido superada. AbstractThe purpose of this review article is to discuss a broaden notion of urban void, in the context of urban sprawl and the urgent need for sustainable land management. To draw a picture about the topic of urban voids, this essay has been divided into two sections. The first part presents a number of concepts that justify a global response of reclaiming urban voids. The second section contains a compilation of case studies of urban void management, whose viability has been possible because innovative urban legislation tools. In the Costa Rican context, where the little discussion about urban voids remain housed in negative urban imaginary, it is important to make visible experiences where its negative meaning has been overcome.

Urban sprawl and the ‘olive’ landscape: sustainable land management for ‘crisis’ cities

GeoJournal ◽  
2018 ◽  
Vol 84 (1) ◽  
pp. 237-255 ◽  
Author(s):  
Massimo Cecchini ◽  
Ilaria Zambon ◽  
Antonella Pontrandolfi ◽  
Rosario Turco ◽  
Andrea Colantoni ◽  
...  
Keyword(s):  
Land Management ◽  
Urban Sprawl ◽  
Sustainable Land Management

Indicators of land quality and sustainable land management

1998 ◽  
Author(s):  
Julian Dumanski ◽  
Samuel Gameda ◽  
Christian Pieri ◽  
Keyword(s):  
Land Management ◽  
Sustainable Land Management ◽  
Land Quality

scholarly journals How to train your robot with deep reinforcement learning: lessons we have learned

2021 ◽  
pp. 027836492098785
Author(s):  
Julian Ibarz ◽  
Jie Tan ◽  
Chelsea Finn ◽  
Mrinal Kalakrishnan ◽  
Peter Pastor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Case Studies ◽  
Real World ◽  
Review Article ◽  
The Real ◽  
Complex Skills ◽  
Real World Learning ◽  
Level Sensor ◽  
Embodied Agent

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.

scholarly journals Relationships and the Determinants of Sustainable Land Management Technologies in North Gojjam Sub-Basin, Upper Blue Nile, Ethiopia

2021 ◽  
Vol 13 (11) ◽  
pp. 6365
Author(s):  
Alelgn Ewunetu ◽  
Belay Simane ◽  
Ermias Teferi ◽  
Benjamin F. F. Zaitchik
Keyword(s):  
Land Management ◽  
Scale Up ◽  
Policy Issue ◽  
Farm Households ◽  
Farm Size ◽  
Farm Income ◽  
Sustainable Land Management ◽  
Related Factors ◽  
Blue Nile ◽  
The North

Sustainable land management (SLM) is a leading policy issue in Ethiopia. However, the adoption and continuous use of SLM technologies remain low. This study investigates the interrelationship of adopted SLM technologies and key factors of farmers’ decisions to use SLM technologies in the North Gojjam sub-basin of the Upper Blue Nile. The study was based on the investigation of cross-sectional data obtained from 414 randomly selected rural household heads, focus group discussions, and key informant interviews. Descriptive statistics and Econometric models (i.e., Multivariate Probit and Poisson regression) were used to analyze quantitative data, while a content analysis method was used for qualitative data analysis. Results indicate that at least one type of SLM technology was implemented by 94% of farm households in the North Gojjam sub-basin. The most widely used technologies were chemical fertilizer, soil bund, and animal manure. Most of the adopted SLM technologies complement each other. Farm size, family size, male-headed household, local institutions, perception of soil erosion, livestock size, total income, and extension service increased the adoption probability of most SLM technologies. Plot fragmentation, household age, plot distance, off-farm income, market distance, and perception of good fertile soil discourage the adoption probability of most SLM technologies. To scale up SLM technologies against land degradation, it is important to consider households’ demographic characteristics, the capacity of farm households, and plot-level related factors relevant to the specific SLM technologies being promoted.

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