Abstract. High location accuracy is a major requirement for satellite image users. Target performance is usually achieved thanks to either specific on-board satellite equipment or an auxiliary registration reference dataset. Both methods may be expensive and with certain limitations in terms of performance. The Institut national de l’information géographique et forestière (IGN) and Airbus Defence and Space (ADS) have worked together for almost 20 years, to build reference data for improving image location using multi-satellite observations. The first geometric foundation created has mainly used SPOT 5 High Resolution Stereoscopic (HRS) imagery, ancillary Ground Control Points (GCP) and Very High Resolution (VHR) imagery, providing a homogenous location accuracy of 10m CE90 almost all over the world in 2010.Space Reference Points (SRP) is a new worldwide 3D GCP database, built from a plethoric SPOT 6/7 multi-view archive, largely automatically processed, with cloud-based technologies. SRP aims at providing a systematic and reliable solution for image location (Unmanned Aerial Vehicle, VHR satellite imagery, High Altitudes Pseudo-Satellite…) and similar topics thanks to a high-density point distribution with a 3m CE90 accuracy.This paper describes the principle of SRP generation and presents the first validation results. A SPOT 6/7 smart image selection is performed to keep only relevant images for SRP purpose. The location of these SPOT 6/7 images is refined thanks to a spatiotriangulation on the worldwide geometric foundation, itself improved where needed. Points making up the future SRP database are afterward extracted thanks to classical feature detection algorithms and with respect to the expected density. Different filtering methods are applied to keep the best candidates. The last step of the processing chain is the formatting of the data to the delivery format, including metadata. An example of validation of SRP concept and specification on two tests sites (Spain and China) is then given. As a conclusion, the on-going production is shortly presented.
Abstract. This paper describes a methodology to produce a 7-classes land cover map of urban areas from very high resolution images and limited noisy labeled data. The objective is to make a segmentation map of a large area (a french department) with the following classes: asphalt, bare soil, building, grassland, mineral material (permeable artificialized areas), forest and water from 20cm aerial images and Digital Height Model.We created a training dataset on a few areas of interest aggregating databases, semi-automatic classification, and manual annotation to get a complete ground truth in each class.A comparative study of different encoder-decoder architectures (U-Net, U-Net with Resnet encoders, Deeplab v3+) is presented with different loss functions.The final product is a highly valuable land cover map computed from model predictions stitched together, binarized, and refined before vectorization.
<p>The extreme events increasingly present in the Pacific (El Nino / La Nina phenomena) have significant consequences on island territories. The effect of climate change and drought episodes is therefore a central concern in many Pacific islands like Vanuatu, Wallis-and-Futuna, French Polynesia, etc. The intense drought events have undeniable impacts on biodiversity, agricultural crops and water resource, as was the case in 2019 for New Caledonia. In particular, projections in New Caledonia count on a possible increase in temperatures of 3&#176;C and a water deficit of 20% in 2100 with longer and more intense drought episodes and an even greater west coast/east coast disparity (Dutheil, 2018). To date, the monitoring and anticipation of these drought episodes is done via meteorological measurements providing information on the rainfall deficit and not on the water stress of plants. In addition, the data are only available on a few measurement points and are not continuous over the territories.</p><p>In order to meet this need, a tool for monitoring environmental and agricultural drought using satellite images and meteorological data is being developed and validated in New Caledonia: Earth Observations for Drought Monitoring (EO4DM) project. This project is carried out in collaboration with M&#233;t&#233;o-France NC as a technical partner and the local Rural Agency as end user, and aims to provide a tool to help decision-making to institutions and management assistance for farmers. This solution will provide data constituting a singularly important source of information whose valuations and contributions can be multiple: agriculture, resource management (water), security (monitoring of risks linked to floods, fires), environment, etc.</p><p>To do so, various surface indices reflecting the state of the vegetation and certain soil properties such as humidity and temperature were estimated from different satellite sensors (MODIS, Sentinel-2, Landsat-8, ASCAT) in order to address different space scales from the field to regional scale. These indices were normalized over a relatively long period, allowing access to drought indicators: VHI (<em>Vegetation Health Index</em>; Kogan et al., 1997), VAI (<em>Vegetation Anomaly Index</em>; Amri et al., 2011), MAI (<em>Moisture Anomaly Index</em>; Amri et al., 2012) or TAI (<em>Temperature Anomaly Index</em>; Le Page and Zribi, 2019). Combined with in-situ meteorological products like SPI (<em>Standardized Precipitation Index; </em>McKee et al., 1993) and SPEI (<em>Standardized Precipitation Evapotranspiration Index;</em> Vicente-Serrano et al., 2010), these indicators assess the intensity of drought episodes and estimate their severity over the entire territory.</p>
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