""It took us about 20 seconds to find the first one in an area indicated by the model," wrote Jaffer in a blog post documenting the field test. Starting at Milton Community Centre, where the model showed high confidence of brambles near the car park, the team systematically visited locations with varying prediction levels. At Milton Country Park, every high-confidence area they checked contained substantial bramble growth."
"When they investigated a residential hotspot, they found an empty plot overrun with brambles. Most amusingly, a major prediction in North Cambridge led them to Bramblefields Local Nature Reserve. True to its name, the area contained extensive bramble coverage. The model reportedly performed best when detecting large, uncovered bramble patches visible from above. Smaller brambles under tree cover showed lower confidence scores-a logical limitation given the satellite's overhead perspective."
A remote-sensing learned representation was used to predict bramble locations and was field-checked at sites around Milton and North Cambridge. High-confidence predictions often matched substantial bramble growth, including a nature reserve and an overrun residential plot. The system performed best detecting large, uncovered patches visible from above and showed lower confidence for brambles obscured by tree cover. The work represents an early proof-of-concept that remains under active research, has not been peer-reviewed, and was validated informally. The approach could offer a lightweight alternative to heavy deep learning models and might run on mobile devices with further development.
Read at Ars Technica
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