DeepC4 is a deep learning-based spatial disaggregation method that uses local census statistics as cluster-level constraints and incorporates multiple conditional label relationships in a multitask learning framework. Applied to Rwandan urban morphology, it achieves macro‑F1 scores of 0.63, 0.78, and 0.45 for roof, wall, and height prediction, respectively, and estimates national dwelling and occupant counts within about 1.1% error compared to census records. The approach outperforms existing GEM and METEOR methods and covers 32‑49% more 500‑meter grid pixels across provinces.
By Joshua Dimasaka, Christian Gei{\ss}, Emily So
arXiv:2609.07982v1 Announce Type: new
Abstract: Standard semi-supervised learning (SSL) typically relies on labelled and unlabelled data sharing a common marginal distribution. This assumption is oft...
By Bright Wiredu Nuakoh, Francky Fouedjio, Stephen Bradshaw, Yaw Kwaafo Awuah-Mensah, Wei Hong Tan, Emet Arya, Ebenezer Afrifa-Yamoah
arXiv:2606. 28390v1 Announce Type: cross Abstract: Geospatial vector data quality is a foundational research topic in GIS, yet classic rule-based quality assessment algorithms often struggle with diverse urban morphologies and massive data volumes.
By Hao Li, Chen Chu, Filip Biljecki, Cyrus Shahabi, Wenwen Li
arXiv:2607. 20559v1 Announce Type: cross Abstract: Monitoring poverty and food security indicators is imperative for addressing socioeconomic challenges in developing nations.
By Aditya Dutt, Paul Gader, Aditya Singh
IPGeoAI is a transformer-based deep learning model that transforms IP geolocation from a static lookup into a sequential modeling task. It captures hierarchical dependencies in IP subnet structures and resolves geographic ambiguity by fusing unstructured semantic context extracted from Autonomous System descriptions via a zero‑shot LLM feature extraction pipeline. Extensive offline evaluation on a proprietary dataset of 200,000 cities shows IPGeoAI outperforms a leading vendor, achieving a 6% improvement in city‑level accuracy and 100% traffic coverage, while online production tests demonstrate a statistically significant +0.35% improvement in first‑tier downstream use‑case metrics.
By Avinash Kadimisetty, Andy Jinqing Yu, Philip Favaloro, Wenlong Liu, Xiaolu Xiong
arXiv:2608.20548v1 Announce Type: cross
Abstract: Disaster damage is spatial: buildings rarely fail in isolation. Yet using spatial context for damage classification remains surprisingly underexplore...
By Fuad Hasan, Chul Min Yeum