This paper presents IITP-VDLand, a comprehensive dataset of Decentraland
parcels sourced from diverse platforms. Unlike existing datasets which have
limited attributes and records, IITP-VDLand offers a rich array of attributes,
encompassing parcel characteristics, trading history, past activities,
transactions, and social media interactions. Alongside, we introduce a key
attribute in the dataset, namely Rarity score, which measures the uniqueness of
each parcel within the virtual world. Addressing the significant challenge
posed by the dispersed nature of this data across various sources, we employ a
systematic approach, utilizing both available APIs and custom scripts, to
gather it. Subsequently, we meticulously curate and organize the information
into four distinct segments: (1) Characteristics Data-Fragment, (2) OpenSea
Trading History Data-Fragment, (3) Ethereum Activity Transactions
Data-Fragment, and (4) Social Media Data-Fragment. We envisage that this
dataset would serve as a robust resource for training machine- and
deep-learning models specifically designed to address real-world challenges
within the domain of Decentraland parcels. The performance benchmarking of more
than 20 state-of-the-art price prediction models on our dataset yields
promising results, achieving a maximum R2 score of 0.8251 and an accuracy of
74.23% in case of Extra Trees Regressor and Classifier. The key findings reveal
that the ensemble models performs better than both deep learning and linear
models for our dataset. We observe a significant impact of coordinates,
geographical proximity, rarity score, and few other economic indicators on the
prediction of parcel prices.

IITP-VDLand 数据集来源于各个平台，包含了 Decentraland 虚拟空间内土地属性、交易历史、活动记录、社交媒体互动等丰富的属性。在该数据集上进行价格预测模型的性能评估显示，集成模型在预测土地价格方面表现更好，相关属性包括坐标、地理接近度、稀有度分数和其他经济指标。