Earth Observation and the New African Rural Datascapes: Defining an
Agenda for Critical Research
- URL: http://arxiv.org/abs/2108.09958v1
- Date: Mon, 23 Aug 2021 06:05:16 GMT
- Title: Earth Observation and the New African Rural Datascapes: Defining an
Agenda for Critical Research
- Authors: Rose Pritchard, Wilhelm Kiwango and Andy Challinor
- Abstract summary: Increasing availability of Earth Observation data could transform the use and governance of African rural landscapes.
Recent years have seen a rapid increase in the development of EO data applications targeted at stakeholders in African agricultural systems.
There is still relatively little critical scholarship questioning how EO data are accessed, presented, disseminated and used in different socio-political contexts.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The increasing availability of Earth Observation data could transform the use
and governance of African rural landscapes, with major implications for the
livelihoods and wellbeing of people living in those landscapes. Recent years
have seen a rapid increase in the development of EO data applications targeted
at stakeholders in African agricultural systems. But there is still relatively
little critical scholarship questioning how EO data are accessed, presented,
disseminated and used in different socio-political contexts, or of whether this
increases or decreases the wellbeing of poorer and marginalized peoples. We
highlight three neglected areas in existing EO-for-development research: (i)
the imaginaries of 'ideal' future landscapes informing deployments of EO data;
(ii) how power relationships in larger EO-for-development networks shape the
distribution of costs and benefits; and (iii) how these larger-scale political
dynamics interact with local-scale inequalities to influence the resilience of
marginalised peoples. We then propose a framework for critical
EO-for-development research drawing on recent thinking in critical data
studies, ICT4D and political ecology.
Related papers
- Data-Centric AI in the Age of Large Language Models [51.20451986068925]
This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs)
We make the key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs.
We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization.
arXiv Detail & Related papers (2024-06-20T16:34:07Z) - Planetary Causal Inference: Implications for the Geography of Poverty [3.4137115855910762]
We first document the growth of interest in using satellite images together with EO data in causal analysis.
We then trace the relationship between spatial statistics and machine learning methods before discussing four ways in which EO data has been used in causal machine learning pipelines.
We conclude by providing a step-by-step workflow for how researchers can incorporate EO data in causal ML analysis going forward.
arXiv Detail & Related papers (2024-05-30T20:48:10Z) - Data Augmentation in Human-Centric Vision [54.97327269866757]
This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks.
It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection.
Our work categorizes data augmentation methods into two main types: data generation and data perturbation.
arXiv Detail & Related papers (2024-03-13T16:05:18Z) - When is Off-Policy Evaluation Useful? A Data-Centric Perspective [60.76880827781716]
evaluating the value of a hypothetical target policy with only a logged dataset is important but challenging.
We propose DataCOPE, a data-centric framework for evaluating a target policy given a dataset.
arXiv Detail & Related papers (2023-11-23T17:13:37Z) - Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs)
We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing.
We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
arXiv Detail & Related papers (2023-09-02T00:32:55Z) - Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric
Visual Data [3.4022338837261525]
We analyze human-centric image geo-diversity on a massive scale using geotagged Flickr images associated with each nation in Africa.
We report the quantity and content of available data with comparisons to population-matched nations in Europe.
We present findings for an othering'' phenomenon as evidenced by a substantial number of images from Africa being taken by non-local photographers.
arXiv Detail & Related papers (2023-08-16T20:12:01Z) - Towards a methodology to consider the environmental impacts of digital
agriculture [0.0]
Agriculture affects global warming, while its yields are threatened by it. Information and communication technology (ICT) is often considered as a potential lever to mitigate this tension.
This research aims at defining a methodology taking into account the environmental footprint of agricultural ICT systems and their required infrastructures.
arXiv Detail & Related papers (2023-05-16T07:58:34Z) - Artificial intelligence to advance Earth observation: a perspective [56.13510552915079]
This article gives a bird's eye view of the essential scientific tools and approaches informing and supporting the transition from raw EO data to usable EO-based information.
We cover the impact of (i) Computer vision; (ii) Machine learning; (iii) Advanced processing and computing; (iv) Knowledge-based AI; (v) Explainable AI and causal inference; (vi) Physics-aware models; (vii) User-centric approaches; and (viii) the much-needed discussion of ethical and societal issues related to the massive use of ML technologies in EO.
arXiv Detail & Related papers (2023-05-15T07:47:24Z) - Big Earth Data and Machine Learning for Sustainable and Resilient
Agriculture [0.0]
This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times.
It introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture.
arXiv Detail & Related papers (2022-11-22T20:58:54Z) - Using Satellite Imagery and Machine Learning to Estimate the Livelihood
Impact of Electricity Access [4.006950662054732]
In many regions of the world, sparse data on key economic outcomes inhibits the development, targeting, and evaluation of public policy.
We demonstrate how advancements in satellite imagery and machine learning can help ameliorate these data and inference challenges.
We show how a combination of satellite imagery and computer vision can be used to develop local-level livelihood measurements appropriate for inferring the causal impact of electricity access on livelihoods.
arXiv Detail & Related papers (2021-09-07T06:14:08Z) - Using satellite imagery to understand and promote sustainable
development [87.72561825617062]
We synthesize the growing literature that uses satellite imagery to understand sustainable development outcomes.
We quantify the paucity of ground data on key human-related outcomes and the growing abundance and resolution of satellite imagery.
We review recent machine learning approaches to model-building in the context of scarce and noisy training data.
arXiv Detail & Related papers (2020-09-23T05:20:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.