RS-ML-FPGA: survey data and analysis workflow for Earth-observation ML

A survey-linked collection of Earth-observation ML/FPGA studies, annotated research data, notebooks and table/figure generation code. The arXiv abstract reports 68 experiments and two taxonomies, while the project README documents the reusable analysis pipeline.

Workflow level n/a Source Mixed sources Outcome Not empirically evaluated here

At a glance

Type

Benchmark / dataset

Workflow level

Workflow unspecified / not applicable

Year

2025

Disciplines

Computer scienceEarth & environmental science

Tasks

Data analysisKnowledge synthesisResearch coding

Entry updated

2026-10-17

Evidence & verification

Source level

Mixed sources
Grounded in a combination of peer-reviewed and non-peer-reviewed sources; see the individual sources.

Outcome verification

Not empirically evaluated here
A documented resource or intended workflow, not a demonstrated academic outcome. No empirical benefit is established by its inclusion.

Sources checked

2026-10-17 (UTC)

Primary evidence scope: arXiv abstract; repository README. Descriptive support and omitted claims are recorded separately in entry-audit.json.

Source-checked does not mean independently reproduced. See the Method page for how these labels are assigned.

Sources (2)

Research applicability

An example of AI-assisted research coding with inspectable survey data and a reusable computational evidence-synthesis pipeline.

Getting started

Editorial, untested: inspect the repository's Dataframes and src directories, set up an isolated environment from its requirements, and examine one figure-generation notebook. The source warns its tag-parsing script can fail on differently structured collections.

Maintenance

Accessed 2026-10-17; operational maintenance, hosting and compatibility not tested.

License

Permissions for article, code and data reuse must be checked separately; no general open-licence or redistribution claim.

Workflow & human oversight

Workflow level intentionally unspecified for a reusable component or conceptual reference; researcher oversight and task-specific validation remain necessary.

Workflow level is not an evidence score; higher is not better evidence.

Approach

The README describes exporting research records to Zotero, screening and tagging them, accessing its API through Python, and deriving dataframes and survey figures. It identifies notebook and table-generation files and acknowledges GitHub Copilot assistance for figure-rendering code.

Limitations

Paper abstract plus the linked repository README reviewed, not the paper body, notebooks, underlying data or executable environment. The README states the original Zotero collection requires an API key. No code execution, corpus completeness, comparative FPGA performance or AI coding benefit was verified. The dataset link includes terminal punctuation in the source and was not followed.

Reproducibility

No installation, code execution or outcome reproduction. Source reading and quote verification are not experimental replication.

Adoption requirements

The README gives a Python/Conda environment setup and requirements-file installation. Reusing the original Zotero-connected pipeline requires collection identifiers, API access and the expected tag structure; inspect permissions and avoid committing private keys. Instructions were not executed.

Comments

No comments yet.

Add a comment

Comments are held for moderation before appearing. Critical or corrective comments are welcome — especially evidence that an entry overstates what has been verified.