This document describes PARASID, an approach for near-real time monitoring of habitat change using neural networks and MODIS data. PARASID aims to monitor changes at continental to global scales with a turnaround time of less than 3 months. It uses machine learning to identify anomalies in NDVI values compared to expected values based on climate and site characteristics, indicating potential human impacts. The approach has been tested on sites in Colombia, Paraguay, and the Amazon, detecting deforestation rates with 76% coverage of Colombia. PARASID provides an early warning system for broad-scale habitat conversion monitoring but not detailed local analyses. Further methodological development and adoption by countries and institutions is planned.