Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings | Paper Citation Record

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This paper cites Arderne, C.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Arderne, C

Reference 1

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This paper cites atledge, G.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings atledge, G

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This paper cites Beck, Niklaus E.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Beck, Niklaus E

Reference 3

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This paper cites Bridging the Digital Divide, pages 283--298.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Bridging the Digital Divide, pages 283--298

Reference 4

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This paper cites Gridded sex-disaggregated school-age population datasets for countries and dependent territories in africa in 2020, February 2022.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Gridded sex-disaggregated school-age population datasets for countries and dependent territories in africa in 2020, February 2022

Reference 5

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This paper cites Adoption and Impact of Earth Observation for the 2030 Agenda for Sustainable Development.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Adoption and Impact of Earth Observation for the 2030 Agenda for Sustainable Development

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This paper cites A simple framework for contrastive learning of visual representations.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings A simple framework for contrastive learning of visual representations

Reference 7

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This paper cites Functional Map of the World.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Functional Map of the World

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Gridded population of the world, version 4 (gpwv4): Population density

Reference 9

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This paper cites Esa's ai4eo initiative: Bridging the gap between the ai and earth observation communities.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Esa's ai4eo initiative: Bridging the gap between the ai and earth observation communities

Reference 10

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings generalizable and accessible approach to machine learning with global satellite imagery

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This paper cites Elvidge, Kimgerly Baugh, Mikhail Zhizhin, Feng ChiHsu, and Tolottama Ghosh.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Elvidge, Kimgerly Baugh, Mikhail Zhizhin, Feng ChiHsu, and Tolottama Ghosh

Reference 12

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Newcomers earth observation guide, 2020

Reference 13

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This paper cites Object-based random forest classification for informal settlements identification in the middle east: Jeddah a case study.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Object-based random forest classification for informal settlements identification in the middle east: Jeddah a case study

Reference 14

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Phileo bench: Evaluating geo-spatial foundation models, 2024

Reference 15

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This paper cites Uncertainty-aware interpretable deep learning for slum mapping and monitoring.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Uncertainty-aware interpretable deep learning for slum mapping and monitoring

Reference 16

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This paper cites Why do tree-based models still outperform deep learning on tabular data?.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Why do tree-based models still outperform deep learning on tabular data?

Reference 17

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This paper cites Improving remote sensing research and education in developing countries: Approaches and recommendations.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Improving remote sensing research and education in developing countries: Approaches and recommendations

Reference 18

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This paper cites Internet world stats - internet penetration in africa.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Internet world stats - internet penetration in africa

Reference 19

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This paper cites Measuring digital development: Facts and figures 2021.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Measuring digital development: Facts and figures 2021

Reference 20

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This paper cites Achieving universal and meaningful digital connectivity setting a baseline and targets for 2030.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Achieving universal and meaningful digital connectivity setting a baseline and targets for 2030

Reference 21

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This paper cites Matthew Davis, David B.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Matthew Davis, David B

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Unresolved cited work

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This paper cites Kennedy, J.R.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Kennedy, J.R

Reference 24

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Unresolved cited work

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Unresolved cited work

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings GHSL Data Package 2023

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This paper cites Establishing an operational and continuous monitoring of global built-up surfaces with the copernicus global human settlement layer.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Establishing an operational and continuous monitoring of global built-up surfaces with the copernicus global human settlement layer

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Noor, V.A

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This paper cites Global fixed broadband and mobile network maps.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Global fixed broadband and mobile network maps

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Sparse spatial autoregressions

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Pettersson, Mohammad Kakooei, Julia Ortheden, Fredrik D

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Multi-source satellite imagery and point of interest data for poverty mapping in east java, indonesia: Machine learning and deep learning approaches

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Learning Transferable Visual Models From Natural Language Supervision

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Learning transferable visual models from natural language supervision, 2021 b

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Artificial Intelligence for Social Good: A Survey

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Mcd12q1 modis/terra+aqua land cover type yearly l3 global 500m sin grid v006

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This paper cites Detecting the linkage between arable land use and poverty using machine learning methods at global perspective.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Detecting the linkage between arable land use and poverty using machine learning methods at global perspective

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This paper cites Tingzon, A.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Tingzon, A

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Unresolved cited work

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Project connect

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This paper cites T wo thirds of the world’s school-age children have no internet access at home, new U N I C E F - I T U report says --- unicef.org.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings T wo thirds of the world’s school-age children have no internet access at home, new U N I C E F - I T U report says --- unicef.org

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Van Dijk

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This paper cites The iNaturalist Species Classification and Detection Dataset.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings The iNaturalist Species Classification and Detection Dataset

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings The role of spatial heterogeneity in detecting urban slums

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This paper cites Deep cosine metric learning for person re-identification.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Deep cosine metric learning for person re-identification

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This paper cites Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks

Reference 50

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This paper cites Combining night time lights in prediction of poverty incidence at the county level.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Combining night time lights in prediction of poverty incidence at the county level

Reference 51

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This paper cites an unresolved cited work.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Unresolved cited work

Reference 52

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This paper cites Gps2vec: Towards generating worldwide gps embeddings.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Gps2vec: Towards generating worldwide gps embeddings

Reference 53

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Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Unresolved cited work

Reference 54

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Observation 2e542a40-5814-4afe-b7b0-e687e8008a1b · outbound

This paper cites Esa worldcover 10 m 2021 v200, October 2022.

Predicting Internet Connectivity in Schools: A Feasibility Study Leveraging Multi-modal Data and Location Encoders in Low-Resource Settings Esa worldcover 10 m 2021 v200, October 2022

Reference 55

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