## 1 Introduction
Low CAI values occur at the beginning of time series (December to May) when crop residue (other crop) are present while the indicator values significantly increase in the proper growing period (May to September). There are several types of convolutional neural networks (CNNs), and all of them can greatly help improve the speed and accuracy of many computer vision tasks. In particular, CNN's 3D models are often used to improve object identification in videos or 3D volumes, such as security camera videos [1] and medical scans of cancerous tissue [2]. Figure 1: Representation of proposed technique for class activation interval (CAI). The fully convolutive CNN trained for image segmentation is able to identify time intervals of the input time series that determines class presence. The CAI can be computed in a single forward step: for the class maize results show data relevance from May to October. The spatiotemporal dimension of Sentinel-2 satellite imagery has many similarities to video; for this reason, we adopt models similar to those used for analysis of videos. In recent years, spatiotemporal data has been addressed through CNN models that follow three main ideas: CNN 2D (e.g., Two-stream ConvNets [3] and Temporal Segment Network (TSN) [4]), CNN 3D (e.g., SFSeg [5] and 3D ResNet [6]) and (2+1)D CNN (e.g., P3D [7] and R(2+1)D [8]). Our proposal partly follows the idea of 3D CNNs used for video data sets, but at the same time uses 2D kernels (i) to create segmentation maps and (ii) to predict the activation intervals of classes in the time domain  
Recently, the performance levels of 3D CNNs in various fields have greatly improved [6] and in addition we have a huge amount of free satellite data [9] that can be easily interpreted by 3D models. Motivated by the success of the [10] 2D-FPN network used for multi-class semantic segmentation, successfully applied also on satellite data for semantic segmentation starting from RGB images [11], in this paper we develop a FPN (3+2)D for multi-class semantic segmentation, based on 3D and 2D convolution layers. In particular the model we proposed was designed for crops semantic segmentation to improve automatic crop recognition accuracy by implementing 3D multi-scale capabilities and to increase the spatial and temporal resolution of thematic product. This model is designed for crops semantic segmentation to improve crops recognition accuracy by implementing 3D multi-scale capabilities and to increase the spatial and temporal resolution of crops. The Feature Pyramid Network (FPN) looks a lot like a U-shaped Convolutional Neural Network (U-Net) [12]. Like the U-Net, the FPN has a lateral connection between the bottom-up pyramid and the top-down pyramid. However, where U-net simply copies the features and adds them, FPN applies a \(1\times 1\) level of convolution before adding them. This allows the bottom-up pyramid called the \"backbone\" to be pretty much anything we want. Due to this greater flexibility of the FPN, we have chosen it and adapted it to our particular (3+2)D problem. U-Net has also been adapted to 3D segmentation problems and has been successfully applied to many 2D and 3D segmentation problems [13]. Unfortunately, we cannot compare directly with the U-Net because we would have to modify it in order to to be able to work on a 3D input and a 2D output at the same time, but knowing that FPN and U-Net are two very similar models and that FPN is more flexible compared to U-Net, we have decided to work only with the FPN. Recents papers by [PERSON] et al. [14; 15] have shown that the convolutional units of various layers of convolutional neural networks (CNNs) actually behave as object detectors despite receiving no supervision over the location of the object. A similar paper [16] proposes a technique for making CNNs more transparent by adding \"visual explanations\" to a large class of models.