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Exploring the limits of transfer learning with a unified text-to-text transformer
Original
Colin Raffel et al · 1910
Earlier work this paper cites.
Language models are few-shot learners
Original
Tom B. Brown et al · 2005
Earlier work this paper cites.
Deep residual learning for image recognition
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Kaiming He et al · 2015
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U-net: Convolutional networks for biomedical image segmentation
Original
Olaf Ronneberger et al · 2015
Earlier work this paper cites.
Humanitarian applications of machine learning with remote-sensing data: review and case study in refugee settlement mapping
John A Quinn et al · 2018
Earlier work this paper cites.
ilastik: interactive machine learning for (bio)image analysis
Stuart Berg et al · 2019
Earlier work this paper cites.
Monitoring sustainable development by means of earth observation data and machine learning: A review
Bruno Ferreira et al · 2020
Earlier work this paper cites.

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Flamingo: a visual language model for few-shot learning, 2022
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2022

A General Purpose Neural Architecture for Geospatial Systems

Rahaman, Nasim, Weiss, Martin, Träuble, Frederik et al.

Understand

Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications.

However, collaboration between these actors is difficult due to the heterogeneous nature of geospatial data modalities (e.g., multi-spectral images of various resolutions, timeseries, weather data) and diversity of tasks (e.g., regression of human activity indicators or detecting forest fires).
In this work, we present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias, pre-trained on large amounts of unlabelled earth observation data in a self-supervised manner.
We envision how such a model may facilitate cooperation between members of the community.

Built on

Exploring the limits of transfer learning with a unified text-to-text transformer
Original
Colin Raffel et al · 1910
Earlier work this paper cites.
Language models are few-shot learners
Original
Tom B. Brown et al · 2005
Earlier work this paper cites.
Deep residual learning for image recognition
Original
Kaiming He et al · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Original
Olaf Ronneberger et al · 2015
Earlier work this paper cites.
Humanitarian applications of machine learning with remote-sensing data: review and case study in refugee settlement mapping
John A Quinn et al · 2018
Earlier work this paper cites.
ilastik: interactive machine learning for (bio)image analysis
Stuart Berg et al · 2019
Earlier work this paper cites.
Monitoring sustainable development by means of earth observation data and machine learning: A review
Bruno Ferreira et al · 2020
Earlier work this paper cites.

Similar

Earth observation tools and services to increase the effectiveness of humanitarian assistance
Stefan Lang et al · 2020
Cited alongside, same era.
Perceiver IO: A general architecture for structured inputs & outputs
Original
Andrew Jaegle et al · 2021
Cited alongside, same era.
Fully convolutional siamese neural networks for buildings damage assessment from satellite images
Original
Eugene Khvedchenya et al · 2021
Cited alongside, same era.
Creating A coefficient of change in the built environment after a natural disaster
Original
Karla Saldana Ochoa · 2021
Cited alongside, same era.
Deep learning methods for daily wildfire danger forecasting
Original
Ioannis Prapas et al · 2021

Then

Prompt programming for large language models: Beyond the few-shot paradigm
Original
Laria Reynolds et al · 2021
Later among the works it cites.
Detecting earthquakes: a novel deep learning-based approach for effective disaster response
Muhammad Shakeel et al · 2021
Later among the works it cites.
Sustainbench: Benchmarks for monitoring the sustainable development goals with machine learning
Christopher Yeh et al · 2021
Later among the works it cites.
Flamingo: a visual language model for few-shot learning, 2022
Original
Jean-Baptiste Alayrac et al · 2022
Closest in time.
Remote sensing methods for flood prediction: A review
Hafiz Suliman Munawar et al · 2022
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

Learning transferable visual models from natural language supervision

Original

Alec Radford et al · 2021

Cited alongside, same era.

Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities

Claudio Persello et al · 2022

Closest in time.