| 000 | 01064cam a2200253 4500500 | ||
|---|---|---|---|
| 005 | 20250112003243.0 | ||
| 041 | _afre | ||
| 042 | _adc | ||
| 100 | 1 | 0 |
_aKüçükdemirci, Melda _eauthor |
| 700 | 1 | 0 |
_a Landeschi, Giacomo _eauthor |
| 700 | 1 | 0 |
_a Dell’Unto, Nicolo _eauthor |
| 700 | 1 | 0 |
_a Ohlsson, Mattias _eauthor |
| 245 | 0 | 0 | _aMapping Archeological Signs From Airborne Lidar Data Using Deep Neural Networks: Primary Results |
| 260 | _c2021. | ||
| 500 | _a64 | ||
| 520 | _a– Complexity of large-scale Airborne LIDAR data: its processing, and interpretation emerges the necessity of automated analysis with novel techniques.– Detection and documentation of archaeological ruins, hidden in the forests of the Swedish landscape. | ||
| 690 | _aunet | ||
| 690 | _afeature extraction | ||
| 690 | _aairborne LIDAR | ||
| 690 | _aartificial intelligence | ||
| 690 | _adeep neural networks | ||
| 786 | 0 | _nArcheoSciences | 45-1 | 1 | 2021-08-30 | p. 291-293 | 1960-1360 | |
| 856 | 4 | 1 | _uhttps://shs.cairn.info/revue-archeosciences-2021-1-page-291?lang=en |
| 999 |
_c95317 _d95317 |
||