The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

Anthyllis hermanniae subsp. brutia, is an Italian endemic shrub occurring just in one locality by the Calabrian Ionian coast in southern Italy. Objective of this study is to provide data on population numbers, demography and ecology, as well as to identify the main threats on the long term conservation of the target taxon. The survey has been carried out through field census work, sampling biometric and dendrometric data, and applying simple statistics. The population, counting totally 962 individuals, is divided in two stands concentrated in the central part of the distribution area. It is restricted to a narrow belt on the inland part of the dune slope encompassed between an artificial pine forest at the dune top, and the dune-specialized vegetation on the slope. Some biometric insights, such as stem diameter, plant height and number of annual rings, suggest the role of micro-ecology in individual shaping. The most relevant threating factor for the long term conservation of this rare taxon is the rapidly spreading Pinus halepensis subsp. halepensis. Local population of this invasive alien species, deriving from planted individuals, is now invading the A. hermanniae subsp. brutia habitat. Chorological and ecological data here provided should hopefully steer further population dynamics investigation as well as any urgent environment management actions.

Al-Dawaimah is an ancient Canaanite Palestinian village, occupied in 1948 by Israel, and belongs to inframediterranean to thermomediterranean thermotype and arid, semi-arid, and dry ombrotype. The study presents, a region rich in many plant vascular, and it is part of the Palestinian coast, North Africa, the Negev and the Sinai desert, in addition to the mountainous hills of Palestine located west of the Hebron, Jordan River and the Dead Sea. The objective is to identify and update the flora and vegetation in the area of Al-Dawaimah and its neighboring areas in west Hebron of Palestine. Methodology: More than 270 plant specimens have been taken from Al-Dawaimah and surroundings areas, using Braun-Blanquet, Van der Maarel and Salvador River Martinez methods to study the flora, and phytosociological plants, and 214 x 10 plants plots distributed in area were studied. Result and discussion: Three different plant communities were identified, in different environments between arid, dry- subhumid ombrotype and infra-thermomediterranean thermotype, and different soils as (carbon substrates as brown ruinsenas and terra rossa lands, limestone and others), where more than 214 species of plants have been found, of which 45 (20.02%) are endemic species, and in Raunkiaer's life system, trees represent were, (86; 40.18% trees), (34; 15.88% shrubs), (51; 23.83% chamaephytes), (10; 4.67% geophytes), (16; 7.47% phanerophytes), and (12; 6.54% hemicryptophytes). Conclusion: In Al-Dawaimah area, syntaxonomical performance of these associations are: Quercetalia ilicis Br.-Bl. ex Molinier 1934. Rhamno lycioidis-Quercion cocciferae Rivas Goday ex Rivas-Martinez 1975. 1. Rhamnus palaestinae- Quercetum calliprini ass. nova., Pistacio lentisci -Rhamnetalia alaterni Rivas-Martínez 1975. 2. Ceratonio siliquae -Pistacetum lentisci ass. nova., Junipero phoeniceae- Pinon acutisquamae A.V. Pérez et Cabezudo in A.V. Pérez et al. 1988 corr. Rivas-Martinez. et al. 2002. Pinetalia halepensis Biondi et al. 2014. 3. Junipero phoeniceae- Pinetum halepensis ass. nova.

The reduction of the native forests coverage in mainland Portugal increased in the past centuries, leading to a marked decrease in biodiversity in general, especially on typical species of mature forest environments. However, urban biodiversity seems to resist more effectively than rural to disturbances due to the lower incidence of fires, as well as to agriculture expansion. Thus, in this work, we analyzed the dynamics of the natural vegetation potential in each biogeographic sector, and selected, based on the evolutionary stages of the vegetation, a set of priority taxa for conservation. The criteria used are intended to highlight plants with ornamental value, but at the same time, some of them have high patrimonial value, belonging to the Red List of Vascular Flora of Mainland Portugal or protected by Annexes II, IV and V of the Sectorial Plan of the Natura 2000 Network at the European level. Our analysis resulted in the identification of 62 plants that can be increased in public spaces in order to improve their conservation status. For each biogeographic sector, the plants best adapted to the local edaphoclimatic conditions are presented. Forest habitats can now, through micro-reserves in urban areas, ensure their long-term conservation and greater awareness among the population. An integrated planning, where the socio-ecological strategy is designed for the long term, will benefit the quality of life of citizens in an urban environment. Furthermore, the creation of micro-reserves in urban parks (gardens) can prevent the extinction of many botanical values in the landscapes of the western Mediterranean Basin.