Abstract: The concept of place plays an important role for coupling between human behaviors and geographic environments, and thus provides a human-centric research perspective for geographical analysis. In early place studies, it was difficult to capture and represent the comprehensive semantics of a place in computer systems due to the inherent vagueness, uncertainty, and ambiguity of places, as well as the high cost of obtaining place-based behavioral data at the individual level. Recently, multi-sourced big geo-data and artificial intelligent methods have brought new opportunities for place modeling and sensing. Moreover, place modeling is an important component of knowledge representation in GeoAI. This article reviews the progress of two directions of place modeling and place sensing.Place modeling aims to construct a platial knowledge model that is more in line with human spatial cognition and communication, so as to improve the intelligence and human-computer interaction of geographical information retrieval and spatial query answering. Place sensing aims to capture human perception and cognition about a place to better understand the intrinsic connections between human activities and the geographic environments, and to represent the rich geographical context of a place. The ultimate objective is to construct "white-box" place-based knowledge representation and reasoning for promoting the development of GeoAI.