Analyzing satellite images and remote sensing (RS) data using artificial intelligence (AI)
tools and data fusion strategies has recently opened new perspectives for environmental …

Remote sensing images are useful for a wide variety of planet monitoring applications, from
tracking deforestation to tackling illegal fishing. The Earth is extremely diverse---the amount …

Satellite data has the potential to inspire a seismic shift for machine learning---one in which
we rethink existing practices designed for traditional data modalities. As machine learning …

Rapid development of renewable energy sources, particularly solar photovoltaics (PV), is
critical to mitigate climate change. As a result, India has set ambitious goals to install 500 …

Convolutional neural network (CNN)-based remote sensing (RS) image segmentation has
become a widely used method for building footprint map**. Recently, DeeplabV3+, an …

Geospatial images deliver a visual medium to recognize the prompt changes in the
environment influenced due to seasonal changes. These changes extend their impact on …

Precise coastal shoreline map** is essential for monitoring changes in erosion rates,
surface hydrology, and ecosystem structure and function. Monitoring water bodies in the …

Normalization layers have been shown to improve convergence in deep neural networks,
and even add useful inductive biases. In many vision applications the local spatial context of …

AI for good (AI4G) projects involve develo** and applying artificial intelligence (AI) based
solutions to further goals in areas such as sustainability, health, humanitarian aid, and social …

We propose a method for jointly inferring labels across a collection of data samples, where
each sample consists of an observation and a prior belief about the label. By implicitly …