Hi everyone,
I’m an early-career researcher working primarily with ICESat-2 and other cryosphere datasets, and I’ve been thinking a lot about how AI is changing Earth observation research.
I’ve seen several exciting projects recently (AI-assisted coding, literature review agents, reviewer agents, etc.), but I’m curious about how researchers are actually using these tools in practice, rather than what they could do.
For a typical workflow involving remote sensing or climate datasets, there are many stages:
- Finding appropriate datasets
- Understanding documentation and variables
- Downloading and preprocessing data
- Harmonizing multiple products
- Writing analysis code
- Visualization and statistics
- Literature review and interpretation
I’m curious:
- Which of these stages consumes the most time for you?
- Where have tools like ChatGPT, Claude, Gemini, Copilot, etc. genuinely helped?
- Where do they consistently fall short?
- What do you still find yourself doing manually that you wish you didn’t have to?
- If you could automate one part of your scientific workflow (without sacrificing scientific rigor), what would it be?
For context, in my current work on Southern Ocean sea ice, some of the biggest bottlenecks which take time to resolve are identifying suitable datasets for a scientific question, understanding their conventions, and harmonizing variables across products with different grids, projections, and resolutions.
I’d especially appreciate examples from recent projects (e.g., “I spent two days figuring out projections” or “AI helped with coding but struggled with dataset-specific preprocessing”).
Thanks!