<p>Remote sensing (RS) data is extensively used in the observation and management of surface water and the detection of water bodies for studying ecological and hydrological processes. Small waterbodies are often neglected because of their tiny presence in the image, but being very large in numbers, they significantly impact the ecosystem. However, the detection of small waterbodies in satellite images is challenging because of their varying sizes and tones. In this work, a geospatial artificial intelligence (GeoAI) approach is proposed to detect small water bodies in RS images and generate a spatial map of it along with area statistics. The proposed approach aims to detect waterbodies of different shapes and sizes including those with vegetation cover. For this purpose, a deep neural network (DNN) is trained using the Indian Space Research Organization’s (ISRO) Cartosat—3 multispectral satellite images, which effectively extracts the boundaries of small water bodies with a mean precision of 0.92 and overall accuracy over 96%. A comparative analysis with other popular existing methods using the same data demonstrates the superior performance of the proposed method. The proposed GeoAI approach efficiently generates a map of small water bodies automatically from the input satellite image which can be utilized for monitoring and management of these micro water resources.</p>