Biosensing in the Kampung

Böhlen, Marc; Maharika, Ilya Fadjar; Yin, Ziyan; Hakim, Luqman

doi:10.1109/ie.2014.11

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…The time intensive sense-making effort inherent to this project reminds us that the representation of complex land use conditions across time is not something that remote sensing in conjunction with AI analysis can solve without human assistance. Careful asset creation and science communication remain integral elements of the sense making process, and the communication effort must not only carry a message of unprecedented opportunities but also of limitations, even under state-of-the-art resource observation (Böhlen, 2014). Despite these limitations, the mapping of remote sensing opportunities has the potential to build a shared framework for assessing environmental impacts, and new ways to speak for the forest in the tropics.…”

Section: Discussionmentioning

confidence: 99%

Who Speaks for the Forest? Local Knowledge, Participatory Mapping and Collaborative Evaluation for Gis Analysis in the Tropics of Central Bali, Indonesia

Böhlen

Liu

Iryadi

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This paper describes a land survey of the Bedugul area in Central Bali that was constructed using multi-temporal and multi-spectral data collected from a new class of commercially available satellites to produce detailed land cover mappings. The paper discusses the significance of the land area under investigation, including difficulties in reading land cover features in the tropics. It describes participatory field mapping efforts set in place in order to complement the reading of remote sensing assets and our attempts to represent land use features of particular interest to a local NGO. We further discuss the challenges and opportunities of representing land cover and use scenarios that satellite assets can only partially capture. We use band operations and object-based classification methods to represent change in settlement activity in the area under observation. We also describe a collaborative cloud-based analysis and evaluation pipeline that facilitates the processing of different sources of remote sensing data as well as the representation of various types of land use scenarios defined with the assistance of local knowledge.

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Section: Discussionmentioning

confidence: 99%

Who Speaks for the Forest? Local Knowledge, Participatory Mapping and Collaborative Evaluation for Gis Analysis in the Tropics of Central Bali, Indonesia

Böhlen

Liu

Iryadi

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Second, we invested efforts to ensure that the barriers to system use for constrained research budgets are low by repurposing used business laptops with an open source operating system. Such considerations are essential when working across technology and capital gradients that exist between established and emerging economies [5], [6].…”

Section: Countering Technology Dependenciesmentioning

confidence: 99%

Machine Learning in Ethnobotany

Böhlen

Sujarwo

2020

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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We describe new opportunities created by bring A.I. to the field of ethnobotany. In particular we describe a novel approach to ethnobotany documentation that harnesses machine learning opportunities, specifically for the documentation of traditional ecological knowledge with mobile phones in emerging economies. Using a case study on the island of Bali as a departure point, the project maps out machine learning approaches to documentation and responds to technology and capital gradients between research contexts in the global north and south in an attempt to capture knowledge that might otherwise not be represented.

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“…It has found prototype applications in many domains-for example, ambient assisted living [2,3,15], dialogue systems [4], environmental monitoring [5], smart homes [6], offices [7], transport systems [8], and even smart cities [9]. The theme of this new journal is "Reliable Intelligent Environments".…”

Section: Introductionmentioning

confidence: 99%

“…However, with the exception of a few studies which have focused-in on the reliability of some individual aspects [8,10] and a number of empirical [11] or simulation [12,13] studies, very little attention appears to have been given to investigating the reliability of such IEs. Such reliability could be essential in safety-critical applications, where the well-being of people [2,3,14,15], animals and/or the natural environment [5] could be at risk in cases of the system failing. Although a distributed system may sometimes display "graceful degradation" when a small number of its components fail, for example through self-reconfiguration or self-healing [1], models of how likely failures of components are, and how serious these are for the performance of the entire system, will still be highly valuable.…”

Section: Introductionmentioning

confidence: 99%

The application of statistical reliability theory in the context of Intelligent Environments: a tutorial review

Hunter

2015

J Reliable Intell Environ

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Intelligent Environments often require the integration of multi-modal sensing and actuating technologies with high performance real-time computation, including artificial intelligence systems for analysis, learning patterns and reasoning. Such systems may be complex, and involve multiple components. However, in order to make them affordable, Intelligent Environments sometimes require many of their individual components to be low-cost. Nevertheless, in many applications-including safety-critical systems, and systems monitoring the health and well-being of vulnerable individuals, it is essential that these Intelligent Environment systems are reliable, which the issue of affordability must not compromise. If such environments are to find real application and deployment in these types of domain, it is necessary to be able to obtain accurate predictions of how probable any potential failure of the system is in any given timeframe, and of statistical parameters regarding the expected time to the first, or between successive, failures. Such quantities must be kept within what are deemed to be acceptable tolerances if the Intelligent Environment is to be suitable for applications in these critical areas, without requiring excessively high levels of human monitoring and/or intervention. In this paper, an introductory overview of statistical reliability theory is presented. The applicability of this to the context of Intelligent Environments-particularly those involving safety critical or other sensitive issues-is discussed, along with how such reliability modelling can be used to influence the design, implementation and application of an Intelligent Environment.

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Biosensing in the Kampung

Cited by 6 publications

References 18 publications

Who Speaks for the Forest? Local Knowledge, Participatory Mapping and Collaborative Evaluation for Gis Analysis in the Tropics of Central Bali, Indonesia

Who Speaks for the Forest? Local Knowledge, Participatory Mapping and Collaborative Evaluation for Gis Analysis in the Tropics of Central Bali, Indonesia

Machine Learning in Ethnobotany

The application of statistical reliability theory in the context of Intelligent Environments: a tutorial review

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