Animated Territorial Data Extractor (ATDE): A Computer-Vision Method for Extracting Territorial Data from Animated Historical Maps
We present Animated Territorial Data Extractor (ATDE), a computer vision tool that extracts quantitative territorial data from animated historical map videos. ATDE employs HSV-based color segmentation, RGB channel filtering, and Direct-Neighbor Filtering to identify and count pixels representing territorial control. Combined with preprocessing for temporal alignment and cross-video scaling, the pipeline converts animated videos into structured time-series data. We demonstrate the tool on ten Chinese dynasties (200 BCE - 1912 CE), producing year-by-year pixel counts that align with expected historical patterns. While not a substitute for authoritative historical datasets, ATDE is well-suited for educational demonstrations, preliminary data exploration, and comparative analysis of territorial dynamics. The tool requires no pre-existing shapefiles and can be applied to any animated map video given seed colors and basic configuration. Code and examples are available on GitHub.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video
Detecting objects in a video is a compute-intensive task. In this paper we propose CaTDet, a system to speedup object detection by leveraging the temporal correlation in video. CaTDet consists of two DNN models that form…
object-detectionObject DetectionLLMatDesign: Autonomous Materials Discovery with Large Language Models
Discovering new materials can have significant scientific and technological implications but remains a challenging problem today due to the enormity of the chemical space. Recent advances in machine learning have enabled…
Unleashing the Potential of Differential Evolution through Individual-Level Strategy Diversity
Since Differential Evolution (DE) is sensitive to strategy choice, most existing variants pursue performance through adaptive mechanisms or intricate designs. While these approaches focus on adjusting strategies over tim…
StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection
Over the years, most research towards defenses against adversarial attacks on machine learning models has been in the image recognition domain. The ML-based malware detection domain has received less attention despite it…
Adversarial RobustnessMalware DetectionMeasuring Territorial Control in Civil Wars Using Hidden Markov Models: A Data Informatics-Based Approach
Territorial control is a key aspect shaping the dynamics of civil war. Despite its importance, we lack data on territorial control that are fine-grained enough to account for subnational spatio-temporal variation and tha…