Tootfinder

Mapping Historic Urban Footprints in France: Balancing Quality, Scalability and AI Techniques
Walid Rabehi, Marion Le Texier, R\'emi Lemoy
https://arxiv.org/abs/2510.02097 h…

Mapping Historic Urban Footprints in France: Balancing Quality, Scalability and AI Techniques
Quantitative analysis of historical urban sprawl in France before the 1970s is hindered by the lack of nationwide digital urban footprint data. This study bridges this gap by developing a scalable deep learning pipeline to extract urban areas from the Scan Histo historical map series (1925-1950), which produces the first open-access, national-scale urban footprint dataset for this pivotal period. Our key innovation is a dual-pass U-Net approach designed to handle the high radiometric and stylis…

Criteria for Credible AI-assisted Carbon Footprinting Systems: The Cases of Mapping and Lifecycle Modeling
Shaena Ulissi, Andrew Dumit, P. James Joyce, Krishna Rao, Steven Watson, Sangwon Suh
https://arxiv.org/abs/2509.00240

Criteria for Credible AI-assisted Carbon Footprinting Systems: The Cases of Mapping and Lifecycle Modeling
As organizations face increasing pressure to understand their corporate and products' carbon footprints, artificial intelligence (AI)-assisted calculation systems for footprinting are proliferating, but with widely varying levels of rigor and transparency. Standards and guidance have not kept pace with the technology; evaluation datasets are nascent; and statistical approaches to uncertainty analysis are not yet practical to apply to scaled systems. We present a set of criteria to validate AI-a…

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Cory Doctorow @… on #AI:
[T]he AI bubble is driven by monopolists who've conquered their markets and have no more growth potential, who are desperate to convince investors that they can continue to grow by moving into…

Mapping Socio-Economic Divides with Urban Mobility Data
Yingche Liu, Mengyang Li
https://arxiv.org/abs/2510.08598 https://arxiv.org/pdf/2510.08598

Mapping Socio-Economic Divides with Urban Mobility Data
The massive digital footprints generated by bike-sharing systems in megacities like Shanghai offer a novel perspective on the urban socio-economic fabric. This study investigates whether these daily mobility patterns can quantitatively map the city's underlying social stratification. To overcome the persistent challenge of acquiring fine-grained socio-economic data, we constructed a multi-layered analytical dataset. We annotated 2,000 raw bike trips with local economic attributes, derived from …

FSMA: Scalable and Reliable LoRa for Non-Terrestrial Networks with Mobile Gateways
Rohith Reddy Vennam, Maiyun Zhang, Raghav Subbaraman, Deepak Vashist, Dinesh Bharadia
https://arxiv.org/abs/2510.02800

FSMA: Scalable and Reliable LoRa for Non-Terrestrial Networks with Mobile Gateways
The proliferation of Low Earth Orbit (LEO) satellites for universal IoT applications and the growing use of drones in emergency services, agriculture, and military operations highlight the transformative potential of non-terrestrial networks (NTN). However, these networks face two key challenges: (1) large coverage footprints that create frequent collisions and (2) moving gateways that cause dynamic links and demand synchronization-free, link-aware transmissions. Existing random access schemes …

Adaptive and Regime-Aware RL for Portfolio Optimization
Gabriel Nixon Raj
https://arxiv.org/abs/2509.14385 https://arxiv.org/pdf/2509.14385

Adaptive and Regime-Aware RL for Portfolio Optimization
This study proposes a regime-aware reinforcement learning framework for long-horizon portfolio optimization. Moving beyond traditional feedforward and GARCH-based models, we design realistic environments where agents dynamically reallocate capital in response to latent macroeconomic regime shifts. Agents receive hybrid observations and are trained using constrained reward functions that incorporate volatility penalties, capital resets, and tail-risk shocks. We benchmark multiple architectures, …