Hasil untuk "Industries. Land use. Labor"

EunJeong Cheon, Do Yeon Shin

As sidewalk delivery robots become increasingly integrated into urban life, this paper begins with a critical provocation: Is robot labor labor? More than a rhetorical question, this inquiry invites closer attention to the social and political arrangements that robot labor entails. Drawing on ethnographic fieldwork across two smart-city districts in Seoul, we examine how delivery robot labor is collectively sustained. While robotic actions are often framed as autonomous and efficient, we show that each successful delivery is in fact a distributed sociotechnical achievement--reliant on human labor, regulatory coordination, and social accommodations. We argue that delivery robots do not replace labor but reconfigure it--rendering some forms more visible (robotic performance) while obscuring others (human and institutional support). Unlike industrial robots, delivery robots operate in shared public space, engage everyday passersby, and are embedded in policy and progress narratives. In these spaces, we identify "robot privilege"--humans routinely yielding to robots--and distinct perceptions between casual observers ("cute") and everyday coexisters ("admirable"). We contribute a conceptual reframing of robot labor as a collective assemblage, empirical insights into South Korea's smart-city automation, and a call for HRI to engage more deeply with labor and spatial politics to better theorize public-facing robots.

Naman Srivastava, Joel D Joy, Yash Dixit et al.

Land Use Land Cover (LULC) mapping is essential for urban and resource planning, and is one of the key elements in developing smart and sustainable cities.This study evaluates advanced LULC mapping techniques, focusing on Look-Up Table (LUT)-based Atmospheric Correction applied to Cartosat Multispectral (MX) sensor images, followed by supervised and semi-supervised learning models for LULC prediction. We explore DeeplabV3+ and Cross-Pseudo Supervision (CPS). The CPS model is further refined with dynamic weighting, enhancing pseudo-label reliability during training. This comprehensive approach analyses the accuracy and utility of LULC mapping techniques for various urban planning applications. A case study of Hyderabad, India, illustrates significant land use changes due to rapid urbanization. By analyzing Cartosat MX images over time, we highlight shifts such as urban sprawl, shrinking green spaces, and expanding industrial areas. This demonstrates the practical utility of these techniques for urban planners and policymakers.

Cella M. Sum, Anna Konvicka, Mona Wang et al.

The tech industry's shifting landscape and the growing precarity of its labor force have spurred unionization efforts among tech workers. These workers turn to collective action to improve their working conditions and to protest unethical practices within their workplaces. To better understand this movement, we interviewed 44 U.S.-based tech worker-organizers to examine their motivations, strategies, challenges, and future visions for labor organizing. These workers included engineers, product managers, customer support specialists, QA analysts, logistics workers, gig workers, and union staff organizers. Our findings reveal that, contrary to popular narratives of prestige and privilege within the tech industry, tech workers face fragmented and unstable work environments which contribute to their disempowerment and hinder their organizing efforts. Despite these difficulties, organizers are laying the groundwork for a more resilient tech worker movement through community building and expanding political consciousness. By situating these dynamics within broader structural and ideological forces, we identify ways for the CSCW community to build solidarity with tech workers who are materially transforming our field through their organizing efforts.

S. Barathkumar, K. Sellamuthu, K. Sathyabama et al.

ABSTRACT Soil is an important resource worldwide with diverse physical, chemical, and biological properties. These properties vary from place to place because ecological variables such as temperature, moisture, and land use vary across different ecosystems. Soil quality has declined, which has led to increased demand for food, which poses significant problems in enhancing agricultural production and promoting environmental sustainability. The traditional methods for analyzing soil nutrients are labor-intensive, tedious, and expensive. The soil properties were effectively analyzed via artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) applications, to explain challenging problems with high accuracy and robustness. To interpret multidimensional data inputs derived from agro-industries and provide farmers with relevant information about crop conditions and soil management. AI can increase crop production by optimizing soil nutrient management. With artificial intelligence technology, farmers can identify potential deficits in soil quality, while Machine learning technologies, such as random forests (RF), support vector machines (SVMs), and Artificial and Deep neural networks (ANN, DNN), were used to generate predictive models on the basis of available soil data and auxiliary ecological variables. This review provides a detailed overview of the diverse AI tools and models used for the detection of various soil properties.

Xun Li, E. Hui, Wei Lang et al.

Abstract Following the reform and opening up of China, the Pearl River Delta (PRD) region became a center of foreign investment due to its comparative advantages of cheap labor costs and low land use prices. The tide of migrant workers, comprising a large surplus rural labor force that flooded into the PRD region, caused a rapid increase in the urban population. From the 1980s to the 2000s, migrant workers were a key force that drove urbanization in China. The utilization of automation technology in production since the 2010s has increased the number of unemployed laborers and shifted the dynamics of urbanization. This study investigated how automation is applied in production processes and its effects on different industries, namely, those related to textiles, electronic information, and home electrical appliance manufacturing; specifically we sought to examine the complex relationship among automation, the labor forces, and urbanization by illustrating the implementation of automation in production processes and its influence on labor forces and urbanization. This study revealed that companies in different industries implement automation to differing degrees and through varied upgrading paths. All industries can ultimately achieve technological transformation and cross-industry development. For labor forces, automation exerts two simultaneous folded effects, namely, the direct replacement of low- to middle-skilled workers and the creation of new jobs. The penetration of automation into manufacturing industries has changed the dynamics of urbanization and the social spatial structure of cities, leading to a polarization of the labor forces and the emergence of “dual cities”.

Guilherme Barreto, Claudia Petry, D. Silveira et al.

Introduction: On a global scale, the obstacles to fruticulture correspond to the lack of skilled labor, the limited amount of available arable land, and the high costs of acquiring fertilizers and pesticides. These inconveniences, linked to environmental impacts and ecotoxicological damage, indicate that scientists, industries, and fruit growers have shown interest in the development of biotools for fruiting crops’ management aiming at orchards’ optimal production, such as biostimulants. This bioinput stimulates plant nutrition processes independently of the product’s nutrient content, aiming to improve efficiency in the use of nutrients, tolerance to abiotic stress, and the quality and availability characteristics of nutrients available in the growth medium. Objective: Thus, this narrative review aims to analyze the state-of-the-art regarding the use of biostimulants in fruticulture, compile information on the proper application of these bioinputs and present alternatives to the diffusion of biostimulants in fruit agroecosystems. The totality of bioestimulants’ action mechanisms still needs to be better understood. Results: The applicability of biostimulants in the management of fruiting crops proved to be a relevant possibility to grant sustainability to production systems in fruticulture and reduce costs, increasing productivity, shelf life, and reducing damage caused by climatic adversities in crops, mainly hydric stress. Conclusions: The development of specific legislation for biostimulants should contribute substantially to generating credibility with farmers in order to differentiate, for example, foliar fertilizers and microbial agents.

Seongwoon Kim, Yong-Yeol Ahn, Jaehyuk Park

The labor market is a complex ecosystem comprising diverse, interconnected entities, such as industries, occupations, skills, and firms. Due to the lack of a systematic method to map these heterogeneous entities together, each entity has been analyzed in isolation or only through pairwise relationships, inhibiting comprehensive understanding of the whole ecosystem. Here, we introduce $\textit{Labor Space}$, a vector-space embedding of heterogeneous labor market entities, derived through applying a large language model with fine-tuning. Labor Space exposes the complex relational fabric of various labor market constituents, facilitating coherent integrative analysis of industries, occupations, skills, and firms, while retaining type-specific clustering. We demonstrate its unprecedented analytical capacities, including positioning heterogeneous entities on an economic axes, such as `Manufacturing--Healthcare'. Furthermore, by allowing vector arithmetic of these entities, Labor Space enables the exploration of complex inter-unit relations, and subsequently the estimation of the ramifications of economic shocks on individual units and their ripple effect across the labor market. We posit that Labor Space provides policymakers and business leaders with a comprehensive unifying framework for labor market analysis and simulation, fostering more nuanced and effective strategic decision-making.

Suman Kunwar, Jannatul Ferdush

As the global population continues to expand, the demand for natural resources increases. Unfortunately, human activities account for 23% of greenhouse gas emissions. On a positive note, remote sensing technologies have emerged as a valuable tool in managing our environment. These technologies allow us to monitor land use, plan urban areas, and drive advancements in areas such as agriculture, climate change mitigation, disaster recovery, and environmental monitoring. Recent advances in AI, computer vision, and earth observation data have enabled unprecedented accuracy in land use mapping. By using transfer learning and fine-tuning with RGB bands, we achieved an impressive 99.19% accuracy in land use analysis. Such findings can be used to inform conservation and urban planning policies.

Yuhao Liu, Pranavesh Panakkal, Sylvia Dee et al.

Cloud occlusion is a common problem in the field of remote sensing, particularly for retrieving Land Surface Temperature (LST). Remote sensing thermal instruments onboard operational satellites are supposed to enable frequent and high-resolution observations over land; unfortunately, clouds adversely affect thermal signals by blocking outgoing longwave radiation emission from the Earth's surface, interfering with the retrieved ground emission temperature. Such cloud contamination severely reduces the set of serviceable LST images for downstream applications, making it impractical to perform intricate time-series analysis of LST. In this paper, we introduce a novel method to remove cloud occlusions from Landsat 8 LST images. We call our method ISLAND, an acronym for Interpolating Land Surface Temperature using land cover. Our approach uses LST images from Landsat 8 (at 30 m resolution with 16-day revisit cycles) and the NLCD land cover dataset. Inspired by Tobler's first law of Geography, ISLAND predicts occluded LST through a set of spatio-temporal filters that perform distance-weighted spatio-temporal interpolation. A critical feature of ISLAND is that the filters are land cover-class aware, making it particularly advantageous in complex urban settings with heterogeneous land cover types and distributions. Through qualitative and quantitative analysis, we show that ISLAND achieves robust reconstruction performance across a variety of cloud occlusion and surface land cover conditions, and with a high spatio-temporal resolution. We provide a public dataset of 20 U.S. cities with pre-computed ISLAND LST outputs. Using several case studies, we demonstrate that ISLAND opens the door to a multitude of high-impact urban and environmental applications across the continental United States.

B. N. Kausik

Labor share, the fraction of economic output accrued as wages, is inexplicably declining in industrialized countries. Whilst numerous prior works attempt to explain the decline via economic factors, our novel approach links the decline to biological factors. Specifically, we propose a theoretical macroeconomic model where labor share reflects a dynamic equilibrium between the workforce automating existing outputs, and consumers demanding new output variants that require human labor. Industrialization leads to an aging population, and while cognitive performance is stable in the working years it drops sharply thereafter. Consequently, the declining cognitive performance of aging consumers reduces the demand for new output variants, leading to a decline in labor share. Our model expresses labor share as an algebraic function of median age, and is validated with surprising accuracy on historical data across industrialized economies via non-linear stochastic regression.

Martin Cubaud, Arnaud Le Bris, Laurence Jolivet et al.

Accurate land use maps, describing the territory from an anthropic utilisation point of view, are useful tools for land management and planning. To produce them, the use of optical images alone remains limited. It is therefore necessary to make use of several heterogeneous sources, each carrying complementary or contradictory information due to their imperfections or their different specifications. This study compares two different approaches i.e. a pre-classification and a post-classification fusion approach for combining several sources of spatial data in the context of land use classification. The approaches are applied on authoritative land use data located in the Gers department in the southwest of France. Pre-classification fusion, while not explicitly modeling imperfections, has the best final results, reaching an overall accuracy of 97% and a macro-mean F1 score of 88%.

Sharon Zukin

The emergence of urban tech economies calls attention to the multidimensional spatiality of ecosystems made up of people and organizations that produce new digital technology. Since the economic crisis of 2008, city governments have aggressively pursued economic growth by nurturing these ecosystems. Elected officials create public-private-nonprofit partnerships to build an “innovation complex” of discursive, organizational, and geographical spaces; they aim not only to jump-start economic growth but to remake the city for a new modernity. But it is difficult to insert tech production space into the complicated urban matrix. Embedded industries and social communities want protection from expanding tech companies and the real estate developers who build for them. City council members, state legislators, and community organizations oppose the city government’s attempts to satisfy Big Tech companies. While the city’s density magnifies conflicts of interest over land-use and labor issues, the covid-19 pandemic raises serious questions about the city’s ability to both oppose Big Tech and keep creating tech jobs.

A. Saparbayev, A. Makulova, N. Bayboltaeva et al.

The article discusses the modeling of grain transportation in the system of grain processing industries. The question of determining the need of vehicles for the maintenance of agricultural work is of great national economic importance. It is associated with the problem of the effective use of material and cash resources, land and labor in grain farming. The resulting task is a difficult experimental task.

Saeed Khan, Md Shahzamal

Geo-tagged tweets can potentially help with sensing the interaction of people with their surrounding environment. Based on this hypothesis, this paper makes use of geotagged tweets in order to ascertain various land uses with a broader goal to help with urban/city planning. The proposed method utilises supervised learning to reveal spatial land use within cities with the help of Twitter activity signatures. Specifically, the technique involves using tweets from three cities of Australia namely Brisbane, Melbourne and Sydney. Analytical results are checked against the zoning data provided by respective city councils and a good match is observed between the predicted land use and existing land zoning by the city councils. We show that geo-tagged tweets contain features that can be useful for land use identification.

Claudio Santos, Maryam Hosseini, João Rulff et al.

The growth of cities calls for regulations on how urban space is used and zoning resolutions define how and for what purpose each piece of land is going to be used. Tracking land use and zoning evolution can reveal a wealth of information about urban development. For that matter, cities have been releasing data sets describing the historical evolution of both the shape and the attributes of land units. The complex nature of zoning code and land-use data, however, makes the analysis of such data quite challenging and often time-consuming. We address these challenges by introducing Urban Chronicles, an open-source web-based visual analytics system that enables interactive exploration of changes in land use patterns. Using New York City's Primary Land Use Tax Lot Output (PLUTO) as an example, we show the capabilities of the system by exploring the data over several years at different scales. Urban Chronicles supports on-the-fly aggregation and filtering operations by using a tree-based data structure that leverages the hierarchical nature of the data set to index the shape and attributes of geographical regions that change over time. We demonstrate the utility of our system through a set of case studies that analyze the impact of Hurricane Sandy on land use attributes, as well as the effects of proposed rezoning plans in Downtown Brooklyn.

N. Sandhu, Arvind Kumar

Rice is the staple food for more than half of the world’s population. Although rice production has doubled in the last 30 years as a result of the development of high-yield, widely adaptable, resource-responsive, semi-dwarf varieties, the threat of a food crisis remains as severe as it was 60 years ago due to the ever-increasing population, water scarcity, labor scarcity, shifting climatic conditions, pest/diseases, loss of productive land to housing, industries, rising sea levels, increasing incidences of drought, flood, urbanization, soil erosion, reduction in soil nutrient status, and environmental issues associated with high-input agriculture. Among these, drought is predicted to be the most severe stress that reduces rice yield. Systematic research on drought over the last 10 years has been conducted across institutes on physiology, breeding, molecular genetics, biotechnology, and cellular and molecular biology. This has provided a better understanding of plant drought mechanisms and has helped scientists to devise better strategies to reduce rice yield losses under drought stress. These include the identification of quantitative trait loci (QTLs) for grain yield under drought as well as many agronomically important traits related to drought tolerance, marker-assisted pyramiding of genetic regions that increase yield under drought, development of efficient techniques for genetic transformation, complete sequencing and annotation of rice genomes, and synteny studies of rice and other cereal genomes. Conventional and marker-assisted breeding rice lines containing useful introgressed genes or loci have been field tested and released as varieties. Still, there is a long way to go towards developing drought-tolerant rice varieties by exploiting existing genetic diversity, identifying superior alleles for drought tolerance, understanding interactions among alleles for drought tolerance and their interaction with genetic backgrounds, and pyramiding the best combination of alleles.

Xun Liang, Qingfeng Guan, Keith C. Clarke et al.

Cellular Automata (CA) are widely used to model the dynamics within complex land use and land cover (LULC) systems. Past CA model research has focused on improving the technical modeling procedures, and only a few studies have sought to improve our understanding of the nonlinear relationships that underlie LULC change. Many CA models lack the ability to simulate the detailed patch evolution of multiple land use types. This study introduces a patch-generating land use simulation (PLUS) model that integrates a land expansion analysis strategy and a CA model based on multi-type random patch seeds. These were used to understand the drivers of land expansion and to investigate the landscape dynamics in Wuhan, China. The proposed model achieved a higher simulation accuracy and more similar landscape pattern metrics to the true landscape than other CA models tested. The land expansion analysis strategy also uncovered some underlying transition rules, such as that grassland is most likely to be found where it is not strongly impacted by human activities, and that deciduous forest areas tend to grow adjacent to arterial roads. We also projected the structure of land use under different optimizing scenarios for 2035 by combining the proposed model with multi-objective programming. The results indicate that the proposed model can help policymakers to manage future land use dynamics and so to realize more sustainable land use patterns for future development. Software for PLUS has been made available at https://github.com/HPSCIL/Patch-generating_Land_Use_Simulation_Model

H. Yi

I. Siddiq, M. Saputra, Sri Untari

Industrialization in rural areas is one of the steps to equalize the development that has been centered in urban areas. Industrialization in rural areas is also a mean to absorb labor in rural areas to minimize excessive urbanization, increase rural incomes, diversify the rural employment, and increase the regional development. On the other hand, the coming of industries in the rural areas also has an impact on the diminishing of agricultural land which supports the life of farmers in the village. This paper discusses the impact of shifting use of agricultural land into industrial land from the space perspective of Henri Lefebvre. This paper explains the relation between rural communities, corporations, and countries in this context. This paper employs literature study method which combines various issues that emerge in the literatures related to the industrialization in the countryside as well as the perspectives of Henri Lefebvre's theory as the analysis. In Henri Lefebvre's point of view, space in modern capitalist society will always be competed. It happens since rural areas has cheaper wage labor and is a potential space for industries because the prices are still much cheaper than urban areas. Keywords—Citizen, Civil Society, Henri Lefebvre, Rural Industrialization, Space

Xuan Yang, Zhengchao Chen, Baipeng Li et al.

The land-use map is an important data that can reflect the use and transformation of human land, and can provide valuable reference for land-use planning. For the traditional image classification method, producing a high spatial resolution (HSR), land-use map in large-scale is a big project that requires a lot of human labor, time, and financial expenditure. The rise of the deep learning technique provides a new solution to the problems above. This paper proposes a fast and precise method that can achieve large-scale land-use classification based on deep convolutional neural network (DCNN). In this paper, we optimize the data tiling method and the structure of DCNN for the multi-channel data and the splicing edge effect, which are unique to remote sensing deep learning, and improve the accuracy of land-use classification. We apply our improved methods in the Guangdong Province of China using GF-1 images, and achieve the land-use classification accuracy of 81.52%. It takes only 13 hours to complete the work, which will take several months for human labor.