Teaching AI to See Cities Socially from Space

Kari Jaaskelainen

16 Jan 2026 — 1 min read

We often map what we can see—buildings, roads, rivers. But what about socially defined places like schools or parks? A new study introduces SocioSeg, a dataset that pairs satellite images with digital maps and pixel-level labels for social categories, organized hierarchically.

Built on top, SocioReasoner is a vision-language reasoning framework that “thinks” across images and text in multiple stages, using reinforcement learning to improve decisions. The result: stronger segmentation of social entities and impressive zero-shot generalization.

Urban socio-semantic segmentation from satellite imagery
Cross-modal recognition + multi-stage reasoning
Outperforms state-of-the-art models
Open dataset and code for researchers and cities

From pixels to places people care about.

Explore the paper: https://arxiv.org/abs/2601.10477v1
Code & data: https://github.com/AMAP-ML/SocioReasoner

Paper: https://arxiv.org/abs/2601.10477v1

Register: https://www.AiFeta.com

#AI #ComputerVision #RemoteSensing #UrbanPlanning #GIS #VisionLanguageModels #Segmentation #OpenData #OpenSource #SatelliteImagery

Meet GRACE: a moral governor for safer, more transparent AI

AI agents are getting powerful—so how do we make sure they do the right thing, not just the effective thing? Meet GRACE, a reason-based moral governor that keeps AI behavior aligned with human norms by separating moral reasoning from goal-driven decision-making. * Moral Module: uses deontic logic and explicit reasons

Why some fine-tuned LLMs miss phishing—and how to fix it

Not all fine-tuned LLMs spot phishing equally. A new study tests Llama 3.1 8B, Gemma 2 9B, and Mistral on high-stakes phishing detection—and uses SHAP and mechanistic interpretability to reveal why models do (or don’t) generalize. * Architecture × data diversity matters: Gemma 2 9B hits state-of-the-art performance (F1

AI that explains itself—by following the science

AI that explains itself—by following the science Black-box AI can be powerful, but it often can’t tell us why it made a decision. Concept Bottleneck Models (CBMs) try to fix that by predicting human-understandable concepts first, then the final answer. The catch: standard CBMs ignore domain-specific cause-and-effect and

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A

TL;DR: Finding reliable answers in genomics is hard. GenomAgent turns one big AI into a coordinating team of specialists—and beats the current leader by 12% on a key benchmark. Genomic facts live across many databases. Standard chatbots struggle because they can’t flexibly query those sources. GeneGPT added

Read more

Meet GRACE: a moral governor for safer, more transparent AI

Why some fine-tuned LLMs miss phishing—and how to fix it

AI that explains itself—by following the science

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A