# Multi-Source Satellite Imagery and Machine Learning
## 2 Related Work  
Satellite imagery offers a synthetic and detailed perspective of geological formations, making it possible to analyze deep structures, map the different rocks, and understand the tectonic processes at work. Much research has been conducted in this area, highlighting the importance of this issue for society. Among them, [PERSON] et al. proposed a method for geological mapping by merging multi-source data (geochemical, geophysical, and remote sensing) within a fully convolutional neural network (FCN) model [6]. This approach captures complex relationships between geological features and obtains precise predictions. The results obtained on the Cuonadong dome, with an overall classification accuracy of 0.96 and an average intersection over union (IoU) index of 0.9, demonstrate the exceptional performance of the proposed model. [PERSON] et al. combine multispectral images (Sentinel-2) and synthetic aperture radar data (Sentinel-1) to take advantage of the complementarities of these two sources [7]. By exploiting the complementarities of Sentinel-1 and Sentinel-2 data, their model learns to extract the most discriminating features for river detection. Using this benchmark dataset, they demonstrated their method's superiority over traditional approaches. The results of their experiment achieved an F1 score of 91.53% on the created dataset. [PERSON] et al. developed SAGTR, an integrated satellite-air-ground system, for faster and more accurate detection of anomalies in mountainous areas, where mine tailings reservoirs require rigorous monitoring, thereby improving environmental risk management [8]. [PERSON] et al. exploited the potential of MODIS satellite data and deep learning (LSTM and attention mechanism) to develop an accurate and reliable grain yield forecasting model [9]. Their results, evaluated on a large set of data, demonstrate the relevance of this approach for agriculture. Although the results are encouraging for China, generalization to other geographic regions may be limited due to differences in climate, soil types, agricultural practices, and grain varieties. [PERSON] et al. developed a deep learning model (CHNET) to map canopy height in French Guiana using multi-source satellite data [10]. The model, integrating environmental data, showed promising results but has limits for very high canopies. The study highlights the importance of improving the quality and analysis of GEDI data to characterize tropical forests better. [PERSON] et al. surveyed soil erosion in gardens in Xishuangbanna, a tropical region of China [11]. Faced with the growing threat to food security and biodiversity, they combined satellite imagery, field surveys, and visual interpretation to map the gardens and their conservation measures finely. Using the Chinese Soil Erosion Model (CSLE), they quantified erosion rates and identified the most vulnerable areas. Their results show that gardens with less than 30% plant cover experience irreversible erosion, responsible for 68.19% of total soil loss. [PERSON] et al. developed an innovative model to map forest fire risk in the Anning Valley of China [12]. By combining environmental data and machine learning techniques, they obtained a robust predictive model with greater than 86% accuracy compared to historical data. This study constitutes a significant advance in the management of forest fires by allowing precise identification of risk areas and, thus, better prevention. However, the transferability of results to other geographic regions may be limited. Risk factors and their relative importance vary considerably depending on climatic, ecological, and socio-economic conditions. [PERSON] et al. developed an innovative method of geological interpretation using remote sensing images [13]. By intelligently merging multi-source data using a deep neural network, they can precisely identify many geological elements, from lithology to glaciers. Extracting features at different scales strengthens the robustness of their model.