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Programs, code, visuals, and data for downloading. Some are shared through GitHub and ObservableHQ.

Fast and Flexible Cartogram (equal-density map) Production

Carto3F, the older version 

Installer for Windows 32bit Program 
Documentation

GeoAI Models for Object Detection in Urban Environment

Supported by PSC-CUNY, Center for Advanced Research on Spatial Information (CARSI, led by Prof. Sean Ahearn), and the MS in GeoInformatics (MGEOi) program, this project explores the application of advanced deep learning models for detecting small urban objects using high-resolution (6-inch) aerial imagery.

The current training phase is exploratory, utilizing a limited subset of the full dataset—merely 500 images—and 100 training epochs. Each image consists of a 1024 × 1024 pixels with oriented bounding boxes for three target feature types: water tanks, cooling towers/AC units, and curb cuts.

Six customized models have been successfully implemented and tested across two leading frameworks—MMDetection and Ultralytics YOLO—within multiple Python computational environments (Windows, macOS, and WSL/Ubuntu). The models demonstrate flexibility and compatibility across diverse GPU configurations, providing a strong foundation for future large-scale experimentation and refinement.

The preliminary results are as follows, and pre-trained PyTorch models that can predict these three features are available upon request.

Base Model GPUs mAP(50)
Overall Water Tank Cooling Tower Curb Cut
Rotated Faster R-CNN * 1/CUDA .545 .794 .551 .290
RoI (Regions of Interest) Transformer 1/CUDA .595 .866 .622 .296
LSKNet (Large Selective Kernel) * 1/CUDA .651 .898 .662 .393
Strip R-CNN 1/CUDA .632 .867 .642 .385
YOLO v11 1/CUDA .787 .948 .798 .616
YOLO v12† M3 Ultra/MPS .645 .929 .635 .370
YOLO v12‡ 3/CUDA .766 .941 .756 .602

*: Models trained with MMDetection and MMRotate frameworks by applying transfer learning techniques.

†: This Yolo v12 model does not use any pre-trained parameters like other models. It was trained using 500 NYC images with 1,600 epochs. Other models use parameters pre-trained from the DOTA V1 dataset, by default.

‡: This model uses pre-trained model parameters that are based on 5,000 NYC images of 1024 by 1024. The pre-training was accomplished by this project with a self-supervised learning (SSL) framework.