Flora Salim

Can satellite VLMs and spatial datasets improve how we map human mobility? 🛰️🏙️
Two of our 9 papers from our lab accepted in #SIGSPATIAL2026 are tackling urban mobility from orbit and from the ground.

🛰️ PAPER 1: Do Satellites See Commuters? A Critical Benchmark of Vision Foundation Models
The Problem: Travel census data is sparse and expensive. Can satellite foundation models replace it globally?
🛠️ Contributions:
• Benchmarked vision encoders (RemoteCLIP, DINOv3, SatCLIP, AlphaEarth) via a WeDAN graph diffusion pipeline across 1,925 US counties, 325 UK districts, and 14 cities.
🎯 Takeaways:
1️⃣ Language vs. Geography: Language-supervised models (RemoteCLIP) dominate locally, recovering 98.3% of census performance. Geographically grounded models (AlphaEarth) win at cross-continental transfers by 33%.
2️⃣ Scale isnt everything: Despite massive datasets, self-supervised models like DINOv3 collapsed to near-zero accuracy globally.
3️⃣ Cross-Continental Wall: Seamlessly transferring mobility logic across radically different global cities remains an unsolved problem.

Proud of my undergrad student Ashiq Shukoor Iqbal. His first paper, jointly authored with Wilson Wongso is a solid output from his 1 year Honours thesis project with us.

Preprint: arxiv.org/abs/2609.00661
Code: github.com/cruiseresearch…

📍PAPER 2: Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks 
Human mobility AI is suffering from a massive data bottleneck. Most models are still trained on outdated check-in datasets from 2012-2013, over-indexed on a few Western tech-hubs, and isolated from modern open-source mapping data.

🛠️ Technical Contributions:
• Massive-STEPS: A newly released, ultra-large-scale semantic trajectory benchmark dataset.
• Expanded temporal and geographic horizons, capturing 24 months of data across two distinct eras (2012-2013 and 2017-2018) spanning 15 global cities—with an emphasis on under-represented markets like Jakarta, Kuwait City, and Petaling Jaya.
• Formally aligned check-in data with Foursquares Open Source Places metadata to benchmark traditional models alongside zero-shot Large Language Models (LLMs).

🎯 Key Takeaways:
• Cultural Nuance Matters: Mobility algorithms trained solely on US patterns fail in Southeast Asian or Middle Eastern contexts; diverse benchmarks are a necessity, not an option.
• Benchmarking LLMs: The cross-era dataset proves that modern LLMs can reason through shifting urban structures, opening new avenues for zero-shot location recommendation. 

Preprint: arxiv.org/abs/2505.11239
Code: github.com/cruiseresearch…
🤗 dataset: huggingface.co/collections/CR…
(total download to date: >26K)

Awesome effort by my PhD student Wilson Wongso.

More #SIGSPATIAL2026 posts to come...

#SpatialAI #ComputerVision #RemoteSensing #DataScience #UrbanMobility

If you're attending #UbiComp in Shanghai this week, find my students Baiyu Chen @Br33z3lled, Tahia Tazin, and Hada Melino Muhammad who are presenting their papers.

1. 🚀 Camera-Free Egocentric HAR: Distilling Video Intelligence into Low-Power Wearable Sensors. (see the 1st fig)
with COMODO (Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition (IMWUT journal), Baiyu Chen et al.

Always-on video in egocentric AI drains batteries, generates high heat, and raises privacy flags. 
COMODO is a self-supervised cross-modal distillation framework that transfers the semantic structure of pretrained video representations to IMU representations through similarity-distribution alignment, enabling efficient and generalisable human activity recognition with IMU-only inference.
COMODO prevents loss of temporal structure during video-to-IMU translation.
By mapping motion data into the same semantic space as video, COMODO enables headworn wearables to track activities without cameras.

Why this matters:
⚡ 115x Compute Drop: Slashes operations from 196B to just 1.7B FLOPs and latency to 75.7ms.
🔋 Battery: Camera sensors can safely duty-cycle while the ultra-low-power IMU runs continuously.
👁️ Privacy: Recognises complex activities without recording user spaces.

paper: arxiv.org/abs/2503.07259
code: github.com/cruiseresearch…
I presented this work also in the invite-only @Meta Wearables summit in July.

2. How stressed are ICU nurses during a shift? 🏥 (ISWC 2026) by Tahia Tazin et al. (see the second figure).
In our study, we tracked 20 Surgical ICU nurses using non-invasive wearable sensors—measuring brainwaves (EEG), sweat gland activity (EDA), and pulse (BVP) during routine workflows. By using multimodal physiological sensing, the system can predict cognitive load in real time, paving the way for smarter, safer clinical environments.
paper: dl.acm.org/doi/10.1145/38…

3. How few self-reported labels does a mobile-sensing model need to adapt to a users mental health? 📱 by Hada Melino Muhammad et al. (see the 3rd figure)
Continuous mental health surveys cause massive user fatigue, leading to high drop-out rates in digital interventions.
The Solution: Models can achieve robust screening accuracy by collecting only a few highly strategic baseline labels rather than continuous daily prompts.
💡 Key Takeaways: The 5-Label Plateau: Accuracy gains drop off heavily after the fifth label.
Base Rate vs. Case-Finding: Early labels mostly teach the model a user’s baseline symptom rate, leaving macro-F1 and ROC-AUC flat.
Zero-Adaptation Baseline: High discrimination is often fully available without personalised tuning.
Lets cut survey burden! 🧠
paper: dl.acm.org/doi/10.1145/38…

#ubicomp2026 #sensing #wearables #smartglasses #TSFM #digitalhealth

So proud of this work led by Breeze. Check this out. #neurips2026

Ok we end up with 9 NeurIPS papers. I'll do a post for each of the 9 NeurIPS papers that got through -- I promise! (One done already - GlucoFM). Congrats to my students, postdocs, and collaborators.

The list of papers.
1. FOGO: Forgetting-aware Orthogonalization Optimiser - Toan Nguyen, YANG LIU et al.
2. GlucoFM - Zechen Li, Ahmed Abdelhadi Metwally, and the Google team.
3. MAGE: Multi-Agent Self-Evolution with Co-Evolutionary Knowledge Graphs - Ruiyi Yang Imran Razzak
4. Robust Residual Correction via Selective Deployment for Time Series Forecasting - Jianxiang Xie, Yuncheng Hua, Hao Xue
5. AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild - Baiyu Chen et al.
6. Discovering Programmatic Policies from Reinforcement Learning-Based Traffic Signal Controllers Lindong Xie
7. Out-of-Distribution Detection in Continual Learning - Nimeshika Udayangani, Sarah M. Erfani, Christopher Leckie (collaboration)

And two evaluation track papers
8. When root cause analysis fails: a retrieval-reranking decomposition - Hada Melino Muhammad, Luan (Miles) Pham
9. Beyond high and low: evaluating graded cognitive diversity in LLM Persona simulation - Sion Weatherhead

NeurIPS Sydney, here we come. :)

#neurips2026 #neurips #ai #foundationmodels #timeseries #llm #tsfm #continuallearning #user #simulation #worldmodel

7 #neurips papers (main track) accepted!! Happy days.... Still waiting for D&B track result...