GIS and Machine Learning for Habitat Protection GIS Lounge

With machine learning having become a typical application along with GIS, one area of focus has been habitat protection. Habitat managers and conservation specialists have struggled to find ways in which to protect wildlife threatened by a variety of mostly-human induced factors. Machine learning and GIS have proven one way in which new ideas and scenarios can be tested before any plan is carried out, saving time, money, and possibly avoiding making crucial habitat errors in plans implemented. A recent example of using GIS and machine learning for habitat protection has been applied on the black-necked crane.[1] This type of bird is very particular with where it can breed and relatively little is known about it.