Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Identification of Maya ruins covered by jungle using Sentinel-1

Domaine:

geospatial

Type de record:

paper
Créateur:
MicHinGuiThi
Éditeur:
Son
Éditeur:
CCSDNat
Hôte:avatar
International audience Archaeologists commonly use airborne LIDAR technology to produce 3D models of structures, even when obscured by a forest canopy. However, this technology has a high cost, both from the plane itself and from the processing of the LIDAR point cloud. Furthermore, this technique can only be used over limited regions. This paper proposes a technique that uses SAR satellite imagery to identify man-made structures hidden by a forest canopy. To do so, we exploit the Ascending and Descending passes of Sentinel-1 so that we obtain two images of the candidate site but from different sight directions. Because of cardinal effects, a large enough building will sign differently from the comparatively isotropic forest canopy it is obscured by. Practically, the technique is based on the ratio of backscattered intensity from these two illumination angles and is well adapted for large areas. The advantages and shortcomings are discussed for the specific case of Sentinel-1 SAR images over two Maya archaeological sites in Central America. Our analysis shows that SAR satellite imagery might provide a free, global-scale way of preselecting sites with large or tall structures to complement LIDAR technology.

Visit

centralesupelec.hal.science

Tasks

computer vision

Languages

Sar

Tags

[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing

Licenses

info:eu-repo/semantics/OpenAccess

Similaires

Mapping Irrigated Croplands in Africa Using Combined Sentinel-1 and Sentinel-2 DataSoil texture estimation using radar and optical data from Sentinel-1 and Sentinel-2Dynamic Coastal Mapping Using Sentinel-1 and Sentinel-2 Data Through Digital Earth AfricaEarly cereal yield prediction using machine learning and Sentinel-1 & Sentinel-2 satellite dataDelineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine LearningSentinel-1 Spatiotemporal Simulation Using Convolutional LSTM for Flood Mapping

Mapping Irrigated Croplands in Africa Using Combined Sentinel-1 and Sentinel-2 Data

In order to effectively manage agriculture and promote sustainable land use in Africa, accurate mapp

Soil texture estimation using radar and optical data from Sentinel-1 and Sentinel-2

[Notes_IRSTEA]1520 [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [ADD1_IRSTEA]Dynamiques spat

Dynamic Coastal Mapping Using Sentinel-1 and Sentinel-2 Data Through Digital Earth Africa

Abstract. Coastal erosion poses a continuous threat to ecosystems, infrastructure, and property. To

Early cereal yield prediction using machine learning and Sentinel-1 & Sentinel-2 satellite data

International audience Forecasting cereal production is crucial for food security, es

Delineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine Learning

Rural communities rely on smallholder maize farms for subsistence agriculture, the main driver of lo

Sentinel-1 Spatiotemporal Simulation Using Convolutional LSTM for Flood Mapping

The synthetic aperture radar (SAR) imagery has been widely applied for flooding mapping based on cha