The data explosion
Tackling the Taboo of Automatic Feature Recognition in Airborne Survey Data
Bibliographic Data
| ID | 3604169 |
|---|---|
| Authors | Rebecca Bennett (0000-0002-0686-9853, University of Winchester), Dave Cowley (0000-0001-8197-260X, Royal Commission on the Ancient and Historical Monuments of Wales), Véronique De Laet (KU Leuven) |
| Year | 2014 |
| Volume | 88 |
| Issue | 341 |
| Pages | 896-905 |
| Publication date | 2014-09-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Antiquity (JOURNAL) |
| Journal identifiers | ISSN: 0003-598X • E-ISSN: 1745-1744 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/s0003598x00050766 |
| OpenAlex | W2281535235 |
| Language | EN |
| Citations received | 41 |
| References cited | 15 |
The increasing availability of multi-dimensional remote-sensing data covering large geographical areas is generating a new wave of landscape-scale research that promises to be as revolutionary as the application of aerial photographic survey during the twentieth century. Data are becoming available to historic environment professionals at higher resolution, greater frequency of acquisition and lower cost than ever before. To take advantage of this explosion of data, however, a paradigm change is needed in the methods used routinely to evaluate aerial imagery and interpret archaeological evidence. Central to this is a fuller engagement with computer-aided methods of feature detection as a viable way to analyse airborne and satellite data. Embracing the new generation of vast datasets requires reassessment of established workflows and greater understanding of the different types of information that may be generated using computer-aided methods
Aerial imagery · Aerial Survey · Cartography · Data science · Database · Feature (linguistics) · Geography · Photogrammetry · Remote sensing · Scale (ratio) · Taboo · Workflow · 3D Surveying and Cultural Heritage · Archaeological Research and Protection · Computer Science · Remote Sensing and LiDAR Applications
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Automated large‐scale mapping and analysis of relict charcoal hearths in Connecticut (USA) using a Deep Learning Yolov4 framework
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Evaluating Mask R‐CNN models to extract terracing across oceanic high islands
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Deploying multispectral remote sensing for multi‐temporal analysis of archaeological crop stress at Ravenshall, Fife, Scotland
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Interpretation of archaeological small-scale features in spectral images
Methods for the extraction of archaeological features from very high-resolution Ikonos-2 remote sensing imagery, Hisar (southwest Turkey)
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Object-based landform delineation and classification from DEMs for archaeological predictive mapping
| Unique citing works | 41 |
|---|---|
| Citations per year | 3,73 |
| Citation span | 2015 - 2026 (12) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 40 |