When considering urban growth in metropolitan areas, polycentric urban structures have been significantly observable everywhere, both in developing and developed nations. Morphological urban developments have been considered in many studies, and recently, attention has been paid to polycentric cities. Polycentricism is a collective concept that works at local, regional, and national levels. However, the development of polycentric urban spaces includes a multitude of decentralized morphologies and functional properties that have significant impacts.

Adverse climatic events like heat stress, floods, unseasonal rainfall, and droughts frequently hinder crop productivity. Long-term crop yield data plays a crucial role in food security planning. This study presents historical wheat yield data at the satellite pixel level from 2001 to 2019 in Uttar Pradesh, India. We use various satellite indicators to develop wheat yield models, including the normalized difference vegetation index and gridded weather data, such as precipitation, temperature, and evapotranspiration.

Slow moving motions are mostly tackled by using the phase information of Synthetic Aperture Radar (SAR) images through Interferometric SAR (InSAR) approaches based on machine and deep learning. Nevertheless, to the best of our knowledge, there is no dataset adapted to machine learning approaches and targeting slow ground motion detections. With this dataset, we propose a new InSAR dataset  for Slow SLIding areas DEtections (ISSLIDE) with machine learning. The dataset is composed of standardly processed interferograms and manual annotations created following geomorphologist strategies.