# Coastal Sargassum Level Estimation from Smartphone Pictures
## 2 Related Work  
Giving the importance of monitoring and assess the impact of Sargassum in coastal ecosystems, automated detection of Sargassum is a research topic that has been investigated for several years. The success of the application of pattern classifiers often depends on the amount of data with which it has been trained and validated. In [17], the process followed for the creation of a geospatial dataset using MODIS (Moderate-Resolution Imaging Spectroradiometer) sensor data are described. The created dataset contains information of the coastline of the state of Quintana Roo from 2014 to 2018, and according to the authors is suitable for the analysis of physical and biological variables in the Caribbean Sea, highlighting its application in the monitoring of Sargassum. Remote sensing has been an important tool for this task. Authors have proposed methods for the automatic detection based on satellite images. In [18], the authors proposed a detection method based on spectral and texture features. In that work, four GLCM (Gray Level Co-occurrence Matrix) measurements and sampling spectrum from typical pixels of high-resolution satellite images were calculated. The 10-dimensional patterns were created from the information of the GLCM, the sampling spectrums, and first principal component. These patterns were used to train an Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel. [PERSON] et al. use the images from the Geostationary KOMPSAT 2B (GK2B) GOCI-II satellite to train and test 3 classification algorithms: decision tree, SVM, and a Gentle adaptive boosting; the models were trained with the GOCI-II Rayleigh-corrected reflectance images. Some authors have proposed the use of indexes that can be directly calculated from spectral images. In the work by [19], images obtained with MSI/Sentinel-2, OLCI/ Sentinel-3 and MODIS/Aqua sensors are used to calculate a new index called NFAI (NormalizedFloating Algae Index) which works at the highest pixel resolution namely 250 m for MODIS, 300 m for OLCI, and 20 m for MSI. An updated version of this method was proposed by [20], using FAI (Floating Algae Index) images obtained from the Multispectral Instruments (MSI) of the Sentinel-2, a series of filters were applied to denoise the images and eliminate possible occlusions due to the presence of clouds. The authors report that their method has a high performance (F-score of 86%) when filtering the images. It should be noted that because multispectral images offer a large number of features, it is possible to propose several indices for locating Sargassum in images. In [21], the authors proposed the Floating Vegetation Index (FVI) which is based on values between the 1.0 \(\upmu\)m and 1.24 \(\upmu\)m bands. Test data were collected by NASA JPL AVIRIS instruments in the Gulf of Mexico area and in the south of the City of San Francisco. This index can only be calculated with recent generation sensors and has not yet been tested with a large amount of data. Four vegetation indexes and one floating algae index were proposed in [7]. The indexes were applied to Landsat 8 imagery, and then a Random Forest algorithm was used for classification. More recently, the use of deep neural networks has proved to be an efficient tool to address different problems [22]. Ecological problems have also been addressed using these models [23], such as identify species, classify animal behaviors, and estimating biodiversity. Application of deep learning to tourist photo can be a useful tool, as present in [16], where a dataset of images collected from TripAdvisor was used to test a method of automatically classifying tourist photos by tourist attractions. In [5], a model called ERISNet, which used convolutional and recurrent neural networks architectures, is proposed to detect this macroalgae using Aqua-MODIS imagery from the coastal zone of Quintana Roo. In [14], a different approach was tested, using images from smartphones and highlighting the contribution of crowdsourcing.