Models (SDMs) predict species across space from spatially explicit features.

We present a multi-temporal, multi-modal remote-sensing dataset for predicting how active wildfires will spread at a resolution of 24 hours. The dataset consists of 13 607 images across 607 fire events in the United States from January 2018 to October 2021. For each fire event, the dataset contains a full time series of daily observations, containing detected active fires and variables related to fuel, topography and weather conditions. The dataset is challenging due to: a) its inputs being multi-temporal, b) the high number of 23 multi-modal input channels, c) highly imbalanced labels and d) noisy labels, due to smoke, clouds, and inaccuracies in the active fire detection.

The spike firing rate is defined as the proportion of non-zero elements in the spike tensor. In Table S1, we present the spike firing rates for all spiking tensors in spike-driven Transformer-8-512. SNNs are theoretically more energy efficient than counterpart ANNs. We employ two types of datasets: static image classification and neuromorphic classification. ImageNet-1K is the most typical static image dataset, which is widely used in the field of image classification.

Since the inception of our planet, the meteorological environment, as reflected through spatio-temporal data, has always been a fundamental factor influencing human life, socio-economic progress, and ecological conservation.

The computational overhead of CluB is 1.2 / 1.3 times that of the BEV -only A detailed comparison is shown in the following table. GPUs and the batch size per GPU is set as 2. Table 2: Ablation study on the effect of the two kinds of object queries for the transformer decoder. Red boxes and green boxes are the predictions and ground-truth, respectively. Transfusion: Robust lidar-camera fusion for 3d object detection with transformers. Fully sparse 3d object detection.